{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:53:23Z","timestamp":1760147603253,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Key Research and Development Project","award":["2019C03088","LGG22F010012"],"award-info":[{"award-number":["2019C03088","LGG22F010012"]}]},{"name":"Zhejiang Province Commonweal Projects","award":["2019C03088","LGG22F010012"],"award-info":[{"award-number":["2019C03088","LGG22F010012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In recent years, with the development of artificial intelligence, smart catering has become one of the most popular research fields, where ingredients identification is a necessary and significant link. The automatic identification of ingredients can effectively reduce labor costs in the acceptance stage of the catering process. Although there have been a few methods for ingredients classification, most of them are of low recognition accuracy and poor flexibility. In order to solve these problems, in this paper, we construct a large-scale fresh ingredients database and design an end-to-end multi-attention-based convolutional neural network model for ingredients identification. Our method achieves an accuracy of 95.90% in the classification task, which contains 170 kinds of ingredients. The experiment results indicate that it is the state-of-the-art method for the automatic identification of ingredients. In addition, considering the sudden addition of some new categories beyond our training list in actual applications, we introduce an open-set recognition module to predict the samples outside the training set as the unknown ones. The accuracy of open-set recognition reaches 74.6%. Our algorithm has been deployed successfully in smart catering systems. It achieves an average accuracy of 92% in actual use and saves 60% of the time compared to manual operation, according to the statistics of actual application scenarios.<\/jats:p>","DOI":"10.3390\/e25020388","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T02:07:44Z","timestamp":1676945264000},"page":"388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Attention-Based Convolutional Neural Network for Ingredients Identification"],"prefix":"10.3390","volume":"25","author":[{"given":"Shi","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8047-0672","authenticated-orcid":false,"given":"Ruixue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5003-7325","authenticated-orcid":false,"given":"Jiakai","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0880-9798","authenticated-orcid":false,"given":"Keqiang","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","first-page":"92","article-title":"A survey on food computing","volume":"52","author":"Min","year":"2019","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018). 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10010075"},{"key":"ref_3","unstructured":"(2022, December 01). Market Research Report, Markets and Markets, Report Code: TC 7894. Available online: https:\/\/www.marketsandmarkets.com\/Market-Reports\/artificial-intelligence-market-74851580.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.jpdc.2017.07.004","article-title":"Fast auto-clean CNN model for online prediction of food materials","volume":"117","author":"Chen","year":"2018","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s44163-022-00022-8","article-title":"Quo vadis artificial intelligence?","volume":"2","author":"Jiang","year":"2022","journal-title":"Discov. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, J., and Ngo, C.W. (2016, January 15\u201319). Deep-based ingredient recognition for cooking recipe retrieval. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2964315"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Christodoulidis, S., Anthimopoulos, M., and Mougiakakou, S. (2015, January 7\u20138). Food recognition for dietary assessment using deep convolutional neural networks. Proceedings of the International Conference on Image Analysis and Processing, Genoa, Italy.","DOI":"10.1007\/978-3-319-23222-5_56"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TMM.2016.2614861","article-title":"Modeling restaurant context for food recognition","volume":"19","author":"Herranz","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, Y., Xu, C., Khanna, N., Boushey, C.J., and Delp, E.J. (2014, January 27\u201330). Analysis of food images: Features and classification. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025555"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neucom.2014.03.017","article-title":"Food image classification using local appearance and global structural information","volume":"140","author":"Nguyen","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Farinella, G.M., Moltisanti, M., and Battiato, S. (2014, January 27\u201330). Classifying food images represented as bag of textons. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7026055"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hoashi, H., Joutou, T., and Yanai, K. (2010, January 13\u201315). Image recognition of 85 food categories by feature fusion. Proceedings of the 2010 IEEE International Symposium on Multimedia, Taichung, Taiwan.","DOI":"10.1109\/ISM.2010.51"},{"key":"ref_13","first-page":"113","article-title":"Food image recognition based on convolutional neural network","volume":"51","author":"Liao","year":"2019","journal-title":"J. South China Norm. Univ. (Nat. Sci. Ed.)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TII.2019.2931148","article-title":"A deep transfer learning solution for food material recognition using electronic scales","volume":"16","author":"Xiao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","article-title":"Recent advances in open set recognition: A survey","volume":"43","author":"Geng","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_18","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 20\u201324). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Beijing, China.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pinheiro, P.O., and Collobert, R. (2015, January 7\u201312). From image-level to pixel-level labeling with convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298780"},{"key":"ref_22","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T. (2015, January 7\u201312). Towards open world recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298799"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T.E. (2016, January 27\u201330). Towards open set deep networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1115\/1.4010337","article-title":"A statistical distribution function of wide applicability","volume":"18","author":"Weibull","year":"1951","journal-title":"J. Appl. Mech."},{"key":"ref_26","unstructured":"Bottou, L. (2012). Neural Networks: Tricks of the Trade, Springer."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hou, S., Feng, Y., and Wang, Z. (2017, January 22\u201329). Vegfru: A domain-specific dataset for fine-grained visual categorization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.66"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.compag.2009.09.002","article-title":"Automatic fruit and vegetable classification from images","volume":"70","author":"Rocha","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zeng, G. (2017, January 3\u20135). Fruit and vegetables classification system using image saliency and convolutional neural network. Proceedings of the 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.","DOI":"10.1109\/ITOEC.2017.8122370"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ge, Z., Demyanov, S., Chen, Z., and Garnavi, R. (2017). Generative openmax for multi-class open set classification. arXiv.","DOI":"10.5244\/C.31.42"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., and Naemura, T. (2019, January 15\u201320). Classification-reconstruction learning for open-set recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00414"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/388\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:37:24Z","timestamp":1760121444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/388"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["e25020388"],"URL":"https:\/\/doi.org\/10.3390\/e25020388","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2023,2,20]]}}}