The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications
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Citations
Cites
1 retracted
Nyabuga, D. O., Song, J., Liu, G., & Adjeisah, M. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/1759111 · Cited by 11
Retracted(2006) · Springer eBooks · Cited by 8,123
Deep learning models for plant disease detection and diagnosis
Ferentinos, K. P. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.01.009 · Cited by 3,390
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
Hyperspectral remote sensing data analysis and future challenges
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
Lu, B., Dao, P., Liu, J., He, Y., & Shang, J. (2020) · Remote Sensing · doi:10.3390/rs12162659 · Cited by 1,231
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
Deep learning based multi-temporal crop classification
Zhong, L., Hu, L., & Zhou, H. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.032 · Cited by 1,024
Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., & Plümer, L. (2010) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2010.06.009 · Cited by 978
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
Hong, D., Yokoya, N., Chanussot, J., & Zhu, X. X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878958 · Cited by 948
Metadata
- Authors
- Khan, A., Vibhute, A. D., Mali, S., & Patil, C. H.
- Venue
- Ecological Informatics
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 69
- Pages
- 101678
- ISSN
- 1574-9541 (ISSN-L)
- OpenAlex
- W4280638333
- Cited by
- 382
- References
- 57
- FWCI
- 10.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. https://app.cochat.ai/papers/10.1016/j.ecoinf.2022.101678
- The paper
- Khan, A., Vibhute, A. D., Mali, S., & Patil, C. H. (2022). A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2022.101678
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.
