Development of automated real-time mango grader using machine vision technique
Abstract Grading of agricultural produce, especially mango, is a significant factor in Bangladesh, crucial for maintaining quality standards in domestic and international markets. This study aims to construct an automated conveyor system with a machine vision system to acquire moving mango images. A...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Agriculture |
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| Online Access: | https://doi.org/10.1007/s44279-025-00281-w |
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| author | Abdullah Al Masum Md. Mahedi Hasan Himel M. Mirazus Salehin Kazi Shakibur Rahman Sahabuddin Ahamed Mahjabin Kabir Md Golam Kibria Bhuiyan Anisur Rahman |
| author_facet | Abdullah Al Masum Md. Mahedi Hasan Himel M. Mirazus Salehin Kazi Shakibur Rahman Sahabuddin Ahamed Mahjabin Kabir Md Golam Kibria Bhuiyan Anisur Rahman |
| author_sort | Abdullah Al Masum |
| collection | DOAJ |
| description | Abstract Grading of agricultural produce, especially mango, is a significant factor in Bangladesh, crucial for maintaining quality standards in domestic and international markets. This study aims to construct an automated conveyor system with a machine vision system to acquire moving mango images. An image processing algorithm was developed to extract the area feature from the captured moving mango images and optimized an ejection system for real-time mango grading based on the extracted area feature of the mango. The images were acquired using image acquisition, and a size-based algorithm developed using MATLAB-GUI, which was both simple and successful in categorizing mangoes into three sizes based on area, achieving accuracy for Large (83.7%), Medium (85.5%), and Small (79.7%). The ejection accuracy was achieved for Small-Medium (83.9%), Small-Large (82.8%), and Large-Medium (90.1%). Finally, when all the mango samples were together, the ejection system achieved an accuracy of 89.1%. The automated system demonstrated its potential, achieving accuracy in the grading of mangoes. The prototype’s have shown the feasibility and potential benefits of future mechanization in mango grading processes in Bangladesh. |
| format | Article |
| id | doaj-art-c81369dfaf044599a67871c787c24de0 |
| institution | Kabale University |
| issn | 2731-9598 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Agriculture |
| spelling | doaj-art-c81369dfaf044599a67871c787c24de02025-08-20T03:43:21ZengSpringerDiscover Agriculture2731-95982025-07-013111210.1007/s44279-025-00281-wDevelopment of automated real-time mango grader using machine vision techniqueAbdullah Al Masum0Md. Mahedi Hasan Himel1M. Mirazus Salehin2Kazi Shakibur Rahman3Sahabuddin Ahamed4Mahjabin Kabir5Md Golam Kibria Bhuiyan6Anisur Rahman7Department of Agricultural Mechanization, Habiganj Agricultural UniversityDepartment of Farm Power and Machinery, Bangladesh Agricultural UniversityDepartment of Farm Power and Machinery, Bangladesh Agricultural UniversityDepartment of Farm Power and Machinery, Bangladesh Agricultural UniversityDepartment of Farm Power and Machinery, Bangladesh Agricultural UniversityDepartment of Farm Power and Machinery, Bangladesh Agricultural UniversityPrincipal Scientific Officer, Farm Machinery and Postharvest Technology Division, Bangladesh Rice Research Institute (BRRI)Department of Farm Power and Machinery, Bangladesh Agricultural UniversityAbstract Grading of agricultural produce, especially mango, is a significant factor in Bangladesh, crucial for maintaining quality standards in domestic and international markets. This study aims to construct an automated conveyor system with a machine vision system to acquire moving mango images. An image processing algorithm was developed to extract the area feature from the captured moving mango images and optimized an ejection system for real-time mango grading based on the extracted area feature of the mango. The images were acquired using image acquisition, and a size-based algorithm developed using MATLAB-GUI, which was both simple and successful in categorizing mangoes into three sizes based on area, achieving accuracy for Large (83.7%), Medium (85.5%), and Small (79.7%). The ejection accuracy was achieved for Small-Medium (83.9%), Small-Large (82.8%), and Large-Medium (90.1%). Finally, when all the mango samples were together, the ejection system achieved an accuracy of 89.1%. The automated system demonstrated its potential, achieving accuracy in the grading of mangoes. The prototype’s have shown the feasibility and potential benefits of future mechanization in mango grading processes in Bangladesh.https://doi.org/10.1007/s44279-025-00281-wMangoReal-time gradingMachine visionSurface area |
| spellingShingle | Abdullah Al Masum Md. Mahedi Hasan Himel M. Mirazus Salehin Kazi Shakibur Rahman Sahabuddin Ahamed Mahjabin Kabir Md Golam Kibria Bhuiyan Anisur Rahman Development of automated real-time mango grader using machine vision technique Discover Agriculture Mango Real-time grading Machine vision Surface area |
| title | Development of automated real-time mango grader using machine vision technique |
| title_full | Development of automated real-time mango grader using machine vision technique |
| title_fullStr | Development of automated real-time mango grader using machine vision technique |
| title_full_unstemmed | Development of automated real-time mango grader using machine vision technique |
| title_short | Development of automated real-time mango grader using machine vision technique |
| title_sort | development of automated real time mango grader using machine vision technique |
| topic | Mango Real-time grading Machine vision Surface area |
| url | https://doi.org/10.1007/s44279-025-00281-w |
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