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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

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Reference TypeJournal (article/letter/editorial)
TitleStrategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach
JournalApplied Sciences
AuthorsLi, YongxinAuthor
Zhang, ChaolongAuthor
Xu, HuiAuthor
Yang, YuantongAuthor
Lu, HanAuthor
Deng, LeiAuthor
Year2025 (May 1)Volume15
Issue9
PublisherMDPI AG
DOIdoi:10.3390/app15095036Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID18367367Long-form Identifiermindat:1:5:18367367:5
GUID0
Full ReferenceLi, 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 TextLi, 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

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Not Yet Imported: - journal-article : 10.1007/s11694-023-02293-w

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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
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Not Yet Imported: - proceedings-article : 10.1109/CVPR.2017.690

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Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
Not Yet Imported: - proceedings-article : 10.1109/CVPR.2016.90

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Not Yet Imported: - journal-article : 10.1109/TPAMI.2016.2577031

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Not Yet Imported: - journal-article : 10.1109/TPAMI.2016.2572683

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Not Yet Imported: - journal-article : 10.4114/intartif.vol25iss70pp64-76

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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

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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

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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

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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

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Not Yet Imported: Sensors - journal-article : 10.3390/s23031656

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