Defect detection method of red globe grapes bunches based on near infrared camera imaging
Objective: The study aimed to explore a fast and accurate method to detect brown spot and damage decay in grape bunches. Methods: Colour images （RGB） and near-infrared images （NIR） of red globe grapes bunches were captured by a near-infrared industrial camera. The edges of the samples and the edges...
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| Format: | Article |
| Language: | English |
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The Editorial Office of Food and Machinery
2023-04-01
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| Series: | Shipin yu jixie |
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| Online Access: | http://www.ifoodmm.com/spyjxen/article/abstract/20230125 |
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| author | GAO Sheng |
| author_facet | GAO Sheng |
| author_sort | GAO Sheng |
| collection | DOAJ |
| description | Objective: The study aimed to explore a fast and accurate method to detect brown spot and damage decay in grape bunches. Methods: Colour images （RGB） and near-infrared images （NIR） of red globe grapes bunches were captured by a near-infrared industrial camera. The edges of the samples and the edges of the defective parts were first extracted by applying the Sobel algorithm to the NIR images （NIR）, and then the images were binarized by the adaptive thresholding algorithm to achieve the segmentation of the images. Then the sample edges and fruit stalks were removed by the normalized supergreen method and the finding large connected domain algorithm to extract the shape feature parameters such as roundness, rectangularity and external rectangular aspect ratio of the defective part of red globe grapes bunches and fruit edges, respectively. Finally, a classification model based on BP neural network and support vector machine was developed to discriminate the defective parts and fruit edges. The model enables the rejection of kernel edges to obtain image information of brown spots and damage decay. Results: Using the above-mentioned testing method to verify 60 samples, the accuracy of discriminating red globe grape bunches with intact appearance was as high as 90.00%, those with defects reached 93.33%, and the overall discriminating accuracy reached 91.67%. Conclusion: The study established a method to detect brown spot and damage decay images to enable grading and selection of red globe grapes. |
| format | Article |
| id | doaj-art-ee15efb05ca4478789e0c7980c27aa9e |
| institution | OA Journals |
| issn | 1003-5788 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | The Editorial Office of Food and Machinery |
| record_format | Article |
| series | Shipin yu jixie |
| spelling | doaj-art-ee15efb05ca4478789e0c7980c27aa9e2025-08-20T02:22:37ZengThe Editorial Office of Food and MachineryShipin yu jixie1003-57882023-04-0139114615110.13652/j.spjx.1003.5788.2022.80409Defect detection method of red globe grapes bunches based on near infrared camera imagingGAO Sheng0 Qingdao University of Technology, School of Information and Control Engineering, Qingdao, Shandong 266520 , China Objective: The study aimed to explore a fast and accurate method to detect brown spot and damage decay in grape bunches. Methods: Colour images （RGB） and near-infrared images （NIR） of red globe grapes bunches were captured by a near-infrared industrial camera. The edges of the samples and the edges of the defective parts were first extracted by applying the Sobel algorithm to the NIR images （NIR）, and then the images were binarized by the adaptive thresholding algorithm to achieve the segmentation of the images. Then the sample edges and fruit stalks were removed by the normalized supergreen method and the finding large connected domain algorithm to extract the shape feature parameters such as roundness, rectangularity and external rectangular aspect ratio of the defective part of red globe grapes bunches and fruit edges, respectively. Finally, a classification model based on BP neural network and support vector machine was developed to discriminate the defective parts and fruit edges. The model enables the rejection of kernel edges to obtain image information of brown spots and damage decay. Results: Using the above-mentioned testing method to verify 60 samples, the accuracy of discriminating red globe grape bunches with intact appearance was as high as 90.00%, those with defects reached 93.33%, and the overall discriminating accuracy reached 91.67%. Conclusion: The study established a method to detect brown spot and damage decay images to enable grading and selection of red globe grapes.http://www.ifoodmm.com/spyjxen/article/abstract/20230125 red globe grapes bunches near infrared camera imaging brown spots damage rot non-destructive detection |
| spellingShingle | GAO Sheng Defect detection method of red globe grapes bunches based on near infrared camera imaging Shipin yu jixie red globe grapes bunches near infrared camera imaging brown spots damage rot non-destructive detection |
| title | Defect detection method of red globe grapes bunches based on near infrared camera imaging |
| title_full | Defect detection method of red globe grapes bunches based on near infrared camera imaging |
| title_fullStr | Defect detection method of red globe grapes bunches based on near infrared camera imaging |
| title_full_unstemmed | Defect detection method of red globe grapes bunches based on near infrared camera imaging |
| title_short | Defect detection method of red globe grapes bunches based on near infrared camera imaging |
| title_sort | defect detection method of red globe grapes bunches based on near infrared camera imaging |
| topic | red globe grapes bunches near infrared camera imaging brown spots damage rot non-destructive detection |
| url | http://www.ifoodmm.com/spyjxen/article/abstract/20230125 |
| work_keys_str_mv | AT gaosheng defectdetectionmethodofredglobegrapesbunchesbasedonnearinfraredcameraimaging |