Cite Article

Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques

Choose citation format

BibTeX

@article{IJASEIT14003,
   author = {Akara Thammastitkul and Thananut Klayjumlang},
   title = {Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {6},
   year = {2021},
   pages = {2452--2458},
   keywords = {Mangosteen; image processing; grading; export.},
   abstract = {Accurate quality grading of mangosteen to meet the needs of consumers is very important for improving the value of the export business. Mangosteen fruit ripens quickly after harvesting, and shipping transportation time is a critical factor. Traditional grading methods by physical visual inspection result in delays and human-induced errors. This paper proposed an automatic grading system of mangosteen fruit that utilizes image processing techniques. The maturity stage, class, and size of mangosteen for the export market are analyzed. There are seven stages of maturity from stage one through to six and the under the mature stage, four classes (extra class, class B, class C, and non-standard class) and seven sizes (Jumbo through to Mini). Skin color, skin defect areas, completeness of calyx integrity are also considered. The preprocessing steps consisted of noise removal using a median filter and image enhancement using the grey level transformation. A combination of the mean intensity of red and green images was used to classify the maturation of the fruit. Areas damaged by yellow latex, cracks, and insect pests were extracted, and calyces were counted for class sorting. The length of the diameter was used for size classification. The thresholding, mathematical morphology, and extended minima transform techniques were also used. The average accuracy of the system was 99.54%, with a high accuracy rate for classifying the premium export grades. Results demonstrated that our proposed system was effective and could be used to improve productivity as an accurate and efficient grading method for mangosteen export.},
   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=14003},
   doi = {10.18517/ijaseit.11.6.14003}
}

EndNote

%A Thammastitkul, Akara
%A Klayjumlang, Thananut
%D 2021
%T Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques
%B 2021
%9 Mangosteen; image processing; grading; export.
%! Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques
%K Mangosteen; image processing; grading; export.
%X Accurate quality grading of mangosteen to meet the needs of consumers is very important for improving the value of the export business. Mangosteen fruit ripens quickly after harvesting, and shipping transportation time is a critical factor. Traditional grading methods by physical visual inspection result in delays and human-induced errors. This paper proposed an automatic grading system of mangosteen fruit that utilizes image processing techniques. The maturity stage, class, and size of mangosteen for the export market are analyzed. There are seven stages of maturity from stage one through to six and the under the mature stage, four classes (extra class, class B, class C, and non-standard class) and seven sizes (Jumbo through to Mini). Skin color, skin defect areas, completeness of calyx integrity are also considered. The preprocessing steps consisted of noise removal using a median filter and image enhancement using the grey level transformation. A combination of the mean intensity of red and green images was used to classify the maturation of the fruit. Areas damaged by yellow latex, cracks, and insect pests were extracted, and calyces were counted for class sorting. The length of the diameter was used for size classification. The thresholding, mathematical morphology, and extended minima transform techniques were also used. The average accuracy of the system was 99.54%, with a high accuracy rate for classifying the premium export grades. Results demonstrated that our proposed system was effective and could be used to improve productivity as an accurate and efficient grading method for mangosteen export.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14003
%R doi:10.18517/ijaseit.11.6.14003
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 6
%@ 2088-5334

IEEE

Akara Thammastitkul and Thananut Klayjumlang,"Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, pp. 2452-2458, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.6.14003.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Thammastitkul, Akara
AU  - Klayjumlang, Thananut
PY  - 2021
TI  - Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 6
Y2  - 2021
SP  - 2452
EP  - 2458
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Mangosteen; image processing; grading; export.
N2  - Accurate quality grading of mangosteen to meet the needs of consumers is very important for improving the value of the export business. Mangosteen fruit ripens quickly after harvesting, and shipping transportation time is a critical factor. Traditional grading methods by physical visual inspection result in delays and human-induced errors. This paper proposed an automatic grading system of mangosteen fruit that utilizes image processing techniques. The maturity stage, class, and size of mangosteen for the export market are analyzed. There are seven stages of maturity from stage one through to six and the under the mature stage, four classes (extra class, class B, class C, and non-standard class) and seven sizes (Jumbo through to Mini). Skin color, skin defect areas, completeness of calyx integrity are also considered. The preprocessing steps consisted of noise removal using a median filter and image enhancement using the grey level transformation. A combination of the mean intensity of red and green images was used to classify the maturation of the fruit. Areas damaged by yellow latex, cracks, and insect pests were extracted, and calyces were counted for class sorting. The length of the diameter was used for size classification. The thresholding, mathematical morphology, and extended minima transform techniques were also used. The average accuracy of the system was 99.54%, with a high accuracy rate for classifying the premium export grades. Results demonstrated that our proposed system was effective and could be used to improve productivity as an accurate and efficient grading method for mangosteen export.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14003
DO  - 10.18517/ijaseit.11.6.14003

RefWorks

RT Journal Article
ID 14003
A1 Thammastitkul, Akara
A1 Klayjumlang, Thananut
T1 Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 6
YR 2021
SP 2452
OP 2458
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Mangosteen; image processing; grading; export.
AB Accurate quality grading of mangosteen to meet the needs of consumers is very important for improving the value of the export business. Mangosteen fruit ripens quickly after harvesting, and shipping transportation time is a critical factor. Traditional grading methods by physical visual inspection result in delays and human-induced errors. This paper proposed an automatic grading system of mangosteen fruit that utilizes image processing techniques. The maturity stage, class, and size of mangosteen for the export market are analyzed. There are seven stages of maturity from stage one through to six and the under the mature stage, four classes (extra class, class B, class C, and non-standard class) and seven sizes (Jumbo through to Mini). Skin color, skin defect areas, completeness of calyx integrity are also considered. The preprocessing steps consisted of noise removal using a median filter and image enhancement using the grey level transformation. A combination of the mean intensity of red and green images was used to classify the maturation of the fruit. Areas damaged by yellow latex, cracks, and insect pests were extracted, and calyces were counted for class sorting. The length of the diameter was used for size classification. The thresholding, mathematical morphology, and extended minima transform techniques were also used. The average accuracy of the system was 99.54%, with a high accuracy rate for classifying the premium export grades. Results demonstrated that our proposed system was effective and could be used to improve productivity as an accurate and efficient grading method for mangosteen export.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14003
DO  - 10.18517/ijaseit.11.6.14003