Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine
In this study, an intelligent system based on combined machine vision (MV) and Support Vector Machine (SVM) was developed for sorting of peeled pistachio kernels and shells. The system was composed of conveyor belt, lighting box, camera, processing unit and sorting unit. A color CCD camera was used...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-12-01
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| Series: | Information Processing in Agriculture |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317316300117 |
| _version_ | 1797716826440859648 |
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| author | Hosein Nouri-Ahmadabadi Mahmoud Omid Seyed Saeid Mohtasebi Mahmoud Soltani Firouz |
| author_facet | Hosein Nouri-Ahmadabadi Mahmoud Omid Seyed Saeid Mohtasebi Mahmoud Soltani Firouz |
| author_sort | Hosein Nouri-Ahmadabadi |
| collection | DOAJ |
| description | In this study, an intelligent system based on combined machine vision (MV) and Support Vector Machine (SVM) was developed for sorting of peeled pistachio kernels and shells. The system was composed of conveyor belt, lighting box, camera, processing unit and sorting unit. A color CCD camera was used to capture images. The images were digitalized by a capture card and transferred to a personal computer for further analysis. Initially, images were converted from RGB color space to HSV color ones. For segmentation of the acquired images, H-component in the HSV color space and Otsu thresholding method were applied. A feature vector containing 30 color features was extracted from the captured images. A feature selection method based on sensitivity analysis was carried out to select superior features. The selected features were presented to SVM classifier. Various SVM models having a different kernel function were developed and tested. The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy (99.17%) and then was selected to use in online decision-making unit of the system. By launching the online system, it was found that limiting factors of the system capacity were related to the hardware parts of the system (conveyor belt and pneumatic valves used in the sorting unit). The limiting factors led to a distance of 8 mm between the samples. The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h, respectively. Keywords: Pistachio kernel, Sorting, Machine vision, Sensitivity analysis, Support vector machine |
| first_indexed | 2024-03-12T08:27:18Z |
| format | Article |
| id | doaj.art-ec68d9a4217e4b3090f3bae3606bee8c |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T08:27:18Z |
| publishDate | 2017-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-ec68d9a4217e4b3090f3bae3606bee8c2023-09-02T18:01:17ZengElsevierInformation Processing in Agriculture2214-31732017-12-0144333341Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machineHosein Nouri-Ahmadabadi0Mahmoud Omid1Seyed Saeid Mohtasebi2Mahmoud Soltani Firouz3Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranCorresponding author. Fax: +98 2632808138.; Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranIn this study, an intelligent system based on combined machine vision (MV) and Support Vector Machine (SVM) was developed for sorting of peeled pistachio kernels and shells. The system was composed of conveyor belt, lighting box, camera, processing unit and sorting unit. A color CCD camera was used to capture images. The images were digitalized by a capture card and transferred to a personal computer for further analysis. Initially, images were converted from RGB color space to HSV color ones. For segmentation of the acquired images, H-component in the HSV color space and Otsu thresholding method were applied. A feature vector containing 30 color features was extracted from the captured images. A feature selection method based on sensitivity analysis was carried out to select superior features. The selected features were presented to SVM classifier. Various SVM models having a different kernel function were developed and tested. The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy (99.17%) and then was selected to use in online decision-making unit of the system. By launching the online system, it was found that limiting factors of the system capacity were related to the hardware parts of the system (conveyor belt and pneumatic valves used in the sorting unit). The limiting factors led to a distance of 8 mm between the samples. The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h, respectively. Keywords: Pistachio kernel, Sorting, Machine vision, Sensitivity analysis, Support vector machinehttp://www.sciencedirect.com/science/article/pii/S2214317316300117 |
| spellingShingle | Hosein Nouri-Ahmadabadi Mahmoud Omid Seyed Saeid Mohtasebi Mahmoud Soltani Firouz Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine Information Processing in Agriculture |
| title | Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine |
| title_full | Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine |
| title_fullStr | Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine |
| title_full_unstemmed | Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine |
| title_short | Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine |
| title_sort | design development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S2214317316300117 |
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