The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,133
Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2584107 · Cited by 2,984
Deep Convolutional Neural Networks for Hyperspectral Image Classification
Hu, W., Huang, Y., Wei, L., Zhang, F., & Li, H. (2015) · Journal of Sensors · doi:10.1155/2015/258619 · Cited by 1,941
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Li, Y., Zhang, H., & Shen, Q. (2017) · Remote Sensing · doi:10.3390/rs9010067 · Cited by 1,320
Deep learning classifiers for hyperspectral imaging: A review
Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.09.006 · Cited by 1,073
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
Audebert, N., Le Saux, B., & Lefevre, S. (2019) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2912563 · Cited by 686
Cited by
State of health estimation of lithium-ion battery based on CNN–WNN–WLSTM
Yao, Q., Song, X., & Xie, W. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01300-3 · Cited by 53
Brar, D. S., Aggarwal, A. K., Nanda, V., Kaur, S., Saxena, S., & Gautam, S. (2024) · Food and Humanity · doi:10.1016/j.foohum.2023.11.017 · Cited by 32
Metadata
- Authors
- Liu, Y., Pu, H., & Sun, D.-W.
- Venue
- Trends in Food Science & Technology
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 113
- Pages
- 193-204
- ISSN
- 0924-2244 (ISSN-L)
- OpenAlex
- W3159908420
- Cited by
- 441
- References
- 74
- FWCI
- 18.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. https://app.cochat.ai/papers/10.1016/j.tifs.2021.04.042
- The paper
- Liu, Y., Pu, H., & Sun, D.-W. (2021). Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends in Food Science & Technology. https://doi.org/10.1016/j.tifs.2021.04.042
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
