Deep Learning Based on Residual Networks for Automatic Sorting of Bananas
This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2021/5516368 |
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| _version_ | 1832560281203507200 |
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| author | Abdulkader Helwan Mohammad Khaleel Sallam Ma’aitah Rahib H. Abiyev Selin Uzelaltinbulat Bengi Sonyel |
| author_facet | Abdulkader Helwan Mohammad Khaleel Sallam Ma’aitah Rahib H. Abiyev Selin Uzelaltinbulat Bengi Sonyel |
| author_sort | Abdulkader Helwan |
| collection | DOAJ |
| description | This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented by using transfer learning that uses the stored knowledge of the deep structure. Datasets of bananas have been collected for the implementation of the deep structure. The simulation results of the designed system have shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. The simulation results of the designed residual learning-based system are compared with the results of other systems used for grading the bananas. Comparative results indicate the efficiency of the designed system. The developed system can be used in food processing industry, in real-life applications where the accuracy, cost, and speed of the intelligent system will enhance the production rate and allow meeting the demand of consumers. The system can replace or assist human operators who can exert their energy on the selection of fruits. |
| format | Article |
| id | doaj-art-cd9cbade73e6406b98fa0c6a9f752923 |
| institution | Kabale University |
| issn | 0146-9428 1745-4557 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-cd9cbade73e6406b98fa0c6a9f7529232025-02-03T01:27:58ZengWileyJournal of Food Quality0146-94281745-45572021-01-01202110.1155/2021/55163685516368Deep Learning Based on Residual Networks for Automatic Sorting of BananasAbdulkader Helwan0Mohammad Khaleel Sallam Ma’aitah1Rahib H. Abiyev2Selin Uzelaltinbulat3Bengi Sonyel4School of Engineering, Lebanese American University, Byblos, LebanonDepartment of Management Information Systems, Near East University, Nicosia, TRNC, Mersin 10, TurkeyDepartment of Computer Engineering, Near East University, Nicosia, TRNC, Mersin 10, TurkeyDepartment of Computer Information Systems, Near East University, Nicosia, TRNC, Mersin 10, TurkeyDepartment of Educational Sciences, Eastern Mediterranean University, Famagusta, Mersin 10, TurkeyThis study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented by using transfer learning that uses the stored knowledge of the deep structure. Datasets of bananas have been collected for the implementation of the deep structure. The simulation results of the designed system have shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. The simulation results of the designed residual learning-based system are compared with the results of other systems used for grading the bananas. Comparative results indicate the efficiency of the designed system. The developed system can be used in food processing industry, in real-life applications where the accuracy, cost, and speed of the intelligent system will enhance the production rate and allow meeting the demand of consumers. The system can replace or assist human operators who can exert their energy on the selection of fruits.http://dx.doi.org/10.1155/2021/5516368 |
| spellingShingle | Abdulkader Helwan Mohammad Khaleel Sallam Ma’aitah Rahib H. Abiyev Selin Uzelaltinbulat Bengi Sonyel Deep Learning Based on Residual Networks for Automatic Sorting of Bananas Journal of Food Quality |
| title | Deep Learning Based on Residual Networks for Automatic Sorting of Bananas |
| title_full | Deep Learning Based on Residual Networks for Automatic Sorting of Bananas |
| title_fullStr | Deep Learning Based on Residual Networks for Automatic Sorting of Bananas |
| title_full_unstemmed | Deep Learning Based on Residual Networks for Automatic Sorting of Bananas |
| title_short | Deep Learning Based on Residual Networks for Automatic Sorting of Bananas |
| title_sort | deep learning based on residual networks for automatic sorting of bananas |
| url | http://dx.doi.org/10.1155/2021/5516368 |
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