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Paper Information

Journal:   IRANIAN JOURNAL OF FOOD SCIENCE AND TECHNOLOGY   SUMMER 2015 , Volume 12 , Number 47; Page(s) 141 To 157.
 
Paper: 

OPTIMIZATION OF APPLE FRUIT SORTER PERFORMANCE BY DETECTING BRUISE AND PEDICLE USING MACHINE VISION TECHNIQUE

 
 
Author(s):  KARIMI S., NIKIAN A.*, VELAYATI A.
 
* SHIRAZ UNIVERSITY, SHIRAZ, IRAN
 
Abstract: 

Apple fruit is one of the most worthy garden Product with high nutritional Value and its production in Iran makes more job and Exchange technology. From different apple Non-destructive quality control methods, machine vision technology achieves the more speed, quality, greater productivity and higher valuation for the product. Usually, apple bruise overlaps with Peduncle and in these causes, serious problems of recognition for quality sorting occurs. In this research work it was tried to work out this problem and to increase the sorting systems performance precision. In order to accomplish this, two separate algorithms based on color to identify bruise and pedicle was designed in Matlab. It was achieved 97.14% accuracy for the bruise algorithm and 100% accuracy for the pedicle algorithm. Then with integration of these two algorithms, an algorithm was achieved with 94.29% accuracy. Further experiments to investigate the possibility of increasing the accuracy in detecting bruise with time maintenance was performed by the bruise algorithm. The results indicate that the bruise detection quality by this algorithm gradually increased and after two to three days it reaches the desired consistency. Another algorithm with special properties of bruise and pedicle pictures shape such as roundness value, ratio of area to Perimeter square and also coefficient of variation (cv) of distances of spaced points on the edge from center of gravity of picture was designed. Then bruise and pedicle were distinguished from each other with an accuracy of 100% with this algorithm along with the ANN which it proving the importance of using these techniques, combined with machine vision techniques to increase the accuracy of sorting machines performance.

 
Keyword(s): APPLE BRUISE, MACHINE VISION, ANN, ALGORITHM
 
 
References: 
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Click to Cite.
APA: Copy

KARIMI, S., & NIKIAN, A., & VELAYATI, A. (2015). OPTIMIZATION OF APPLE FRUIT SORTER PERFORMANCE BY DETECTING BRUISE AND PEDICLE USING MACHINE VISION TECHNIQUE. IRANIAN JOURNAL OF FOOD SCIENCE AND TECHNOLOGY, 12(47), 141-157. https://www.sid.ir/en/journal/ViewPaper.aspx?id=407767



Vancouver: Copy

KARIMI S., NIKIAN A., VELAYATI A.. OPTIMIZATION OF APPLE FRUIT SORTER PERFORMANCE BY DETECTING BRUISE AND PEDICLE USING MACHINE VISION TECHNIQUE. IRANIAN JOURNAL OF FOOD SCIENCE AND TECHNOLOGY. 2015 [cited 2021May09];12(47):141-157. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=407767



IEEE: Copy

KARIMI, S., NIKIAN, A., VELAYATI, A., 2015. OPTIMIZATION OF APPLE FRUIT SORTER PERFORMANCE BY DETECTING BRUISE AND PEDICLE USING MACHINE VISION TECHNIQUE. IRANIAN JOURNAL OF FOOD SCIENCE AND TECHNOLOGY, [online] 12(47), pp.141-157. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=407767.



 
 
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