Li, Yongxin; Zhang, Chaolong; Xu, Hui; Yang, Yuantong; Lu, Han; Deng, Lei (2025) Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach. Applied Sciences, 15 (9). doi:10.3390/app15095036
| Reference Type | Journal (article/letter/editorial) | ||
|---|---|---|---|
| Title | Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach | ||
| Journal | Applied Sciences | ||
| Authors | Li, Yongxin | Author | |
| Zhang, Chaolong | Author | ||
| Xu, Hui | Author | ||
| Yang, Yuantong | Author | ||
| Lu, Han | Author | ||
| Deng, Lei | Author | ||
| Year | 2025 (May 1) | Volume | 15 |
| Issue | 9 | ||
| Publisher | MDPI AG | ||
| DOI | doi:10.3390/app15095036Search in ResearchGate | ||
| Generate Citation Formats | |||
| Mindat Ref. ID | 18367367 | Long-form Identifier | mindat:1:5:18367367:5 |
| GUID | 0 | ||
| Full Reference | Li, Yongxin; Zhang, Chaolong; Xu, Hui; Yang, Yuantong; Lu, Han; Deng, Lei (2025) Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach. Applied Sciences, 15 (9). doi:10.3390/app15095036 | ||
| Plain Text | Li, Yongxin; Zhang, Chaolong; Xu, Hui; Yang, Yuantong; Lu, Han; Deng, Lei (2025) Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach. Applied Sciences, 15 (9). doi:10.3390/app15095036 | ||
| In | (2025, May) Applied Sciences Vol. 15 (9). MDPI AG | ||
References Listed
These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.
| Not Yet Imported: - journal-article : 10.1007/s11694-023-02293-w If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Dan (2017) Acta Ecol. Sin. The nitrogen footprint of different scales of restaurant food waste: A Beijing case study 37, 1699 | |
| Lingen (2015) J. Nat. Resour. Study on theories and methods of Chinese food waste 37, 715 | |
| Qiang (2020) J. Arid. Land Resour. Environ. A survey of canteen food waste and its carbon footprint in universities nationwide 34, 49 | |
| Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2022). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv. | |
| Not Yet Imported: - proceedings-article : 10.1109/CVPR.2017.690 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv. | |
| Not Yet Imported: - proceedings-article : 10.1109/CVPR.2016.90 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - journal-article : 10.1109/TPAMI.2016.2577031 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - journal-article : 10.1109/TPAMI.2016.2572683 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Not Yet Imported: - journal-article : 10.4114/intartif.vol25iss70pp64-76 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
![]() | Khoshboresh Masouleh, Mehdi, Shah-Hosseini, Reza (2019) Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 155. 172-186 doi:10.1016/j.isprsjprs.2019.07.009 |
| Mazloumian, A., Rosenthal, M., and Gelke, H. (2022). Deep Learning for Food Waste Classification, Zurich University of Applied Sciences, Institute of Embedded Systems. | |
| Not Yet Imported: - journal-article : 10.1016/j.compbiomed.2021.104699 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Chen (2021) Int. J. Agric. Biol. Eng. Segmentation of field grape bunches via an improved pyramid scene parsing network 14, 185 | |
| Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv. | |
| Berger (2018) Med. Image Underst. Anal. An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation 894, 277 | |
| Not Yet Imported: - journal-article : 10.14309/00000434-201810001-02759 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Duan (2020) Comput. Vis. ECCV Corner Proposal Network for Anchor-free, Two-stage Object Detection 12348, 399 | |
| Not Yet Imported: - proceedings-article : 10.1109/CVPR.2014.81 If you would like this item imported into the Digital Library, please contact us quoting Journal ID | |
| Xiao (2022) IEEE Trans. Pattern Anal. Mach. Intell. Few-shot Object Detection and Viewpoint Estimation for Objects in the Wild 45, 3090 | |
| Not Yet Imported: Lecture Notes in Computer Science - book-chapter : 10.1007/978-3-319-24574-4_28 If you would like this item imported into the Digital Library, please contact us quoting Book ID 9783319245737 | |
| Not Yet Imported: Sensors - journal-article : 10.3390/s23031656 If you would like this item imported into the Digital Library, please contact us quoting Journal ID |
See Also
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