Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach
To ensure the effective implementation of food waste reduction in college cafeterias, Capital Normal University developed an automatic plate recognition system based on machine vision technology. The system operates by obtaining images of plates (whether clean or not) and the diners’ faces through m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5036 |
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| author | Yongxin Li Chaolong Zhang Hui Xu Yuantong Yang Han Lu Lei Deng |
| author_facet | Yongxin Li Chaolong Zhang Hui Xu Yuantong Yang Han Lu Lei Deng |
| author_sort | Yongxin Li |
| collection | DOAJ |
| description | To ensure the effective implementation of food waste reduction in college cafeterias, Capital Normal University developed an automatic plate recognition system based on machine vision technology. The system operates by obtaining images of plates (whether clean or not) and the diners’ faces through multi-directional monitoring, then employs several deep learning models for the automatic localization and identification of the plates. Face recognition technology links the identification results of the plates to the diners. Additionally, the system incorporates innovative educational mechanisms such as online feedback and point redemption to encourage student participation and foster thrifty habits. These initiatives also provide more accurate training samples, enhancing the system’s precision and stability. Our findings indicate that machine vision technology is suitable for rapid identification and location of clean plates. Even without optimized network parameters, the U-Net network demonstrates high recognition accuracy (MIOU of 68.64% and MPA of 78.21%) and ideal convergence speed. Pilot data showed a 13% reduction in overall waste in the cafeteria and over 75% user acceptance of the mechanism. The implementation of this system has significantly improved the efficiency and accuracy of plate recognition, offering an effective solution for food waste prevention in college canteens. |
| format | Article |
| id | doaj-art-546aaad74f254ecaae822d5aeecc0cdd |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-546aaad74f254ecaae822d5aeecc0cdd2025-08-20T03:52:57ZengMDPI AGApplied Sciences2076-34172025-05-01159503610.3390/app15095036Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition ApproachYongxin Li0Chaolong Zhang1Hui Xu2Yuantong Yang3Han Lu4Lei Deng5Logistics Support Department, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment & Tourism, Capital Normal University, Beijing 100048, ChinaLogistics Support Department, Capital Normal University, Beijing 100048, ChinaBeijing Bonade Technology Co., Ltd., Beijing 100040, ChinaCollege of Resources Environment & Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment & Tourism, Capital Normal University, Beijing 100048, ChinaTo ensure the effective implementation of food waste reduction in college cafeterias, Capital Normal University developed an automatic plate recognition system based on machine vision technology. The system operates by obtaining images of plates (whether clean or not) and the diners’ faces through multi-directional monitoring, then employs several deep learning models for the automatic localization and identification of the plates. Face recognition technology links the identification results of the plates to the diners. Additionally, the system incorporates innovative educational mechanisms such as online feedback and point redemption to encourage student participation and foster thrifty habits. These initiatives also provide more accurate training samples, enhancing the system’s precision and stability. Our findings indicate that machine vision technology is suitable for rapid identification and location of clean plates. Even without optimized network parameters, the U-Net network demonstrates high recognition accuracy (MIOU of 68.64% and MPA of 78.21%) and ideal convergence speed. Pilot data showed a 13% reduction in overall waste in the cafeteria and over 75% user acceptance of the mechanism. The implementation of this system has significantly improved the efficiency and accuracy of plate recognition, offering an effective solution for food waste prevention in college canteens.https://www.mdpi.com/2076-3417/15/9/5036food waste preventiondeep learningobject detectioneducationclean plate campaign |
| spellingShingle | Yongxin Li Chaolong Zhang Hui Xu Yuantong Yang Han Lu Lei Deng Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach Applied Sciences food waste prevention deep learning object detection education clean plate campaign |
| title | Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach |
| title_full | Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach |
| title_fullStr | Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach |
| title_full_unstemmed | Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach |
| title_short | Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach |
| title_sort | strategies for automated identification of food waste in university cafeterias a machine vision recognition approach |
| topic | food waste prevention deep learning object detection education clean plate campaign |
| url | https://www.mdpi.com/2076-3417/15/9/5036 |
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