The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress
Open accessAbstract
This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into 'healthy and diseased plant classification' with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.
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Cites
OERKE, E.-C. (2006) · The Journal of Agricultural Science · doi:10.1017/s0021859605005708 · Cited by 5,283
Using Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,026
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Vertex component analysis: a fast algorithm to unmix hyperspectral data
Nascimento, J. M. P., & Dias, J. M. B. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.844293 · Cited by 2,669
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/3289801 · Cited by 2,029
What Is a Savitzky-Golay Filter? [Lecture Notes]
Schafer, R. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.941097 · Cited by 1,786
Mahlein, A.-K. (2016) · Plant Disease · doi:10.1094/pdis-03-15-0340-fe · Cited by 1,363
Savary, S., Ficke, A., Aubertot, J.-N., & Hollier, C. (2012) · Food Security · doi:10.1007/s12571-012-0200-5 · Cited by 1,208
Machine Learning for High-Throughput Stress Phenotyping in Plants
Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016) · Trends in Plant Science · doi:10.1016/j.tplants.2015.10.015 · Cited by 1,109
Cited by
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
Plant disease identification using explainable 3D deep learning on hyperspectral images
Nagasubramanian, K., Jones, S., Singh, A. K., Sarkar, S., Singh, A., & Ganapathysubramanian, B. (2019) · Plant Methods · doi:10.1186/s13007-019-0479-8 · Cited by 394
Khan, A., Vibhute, A. D., Mali, S., & Patil, C. H. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101678 · Cited by 382
Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning
Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., & Kwasniewski, M. T. (2021) · Sensors · doi:10.3390/s21030742 · Cited by 281
Ouhami, M., Hafiane, A., Es-Saady, Y., El Hajji, M., & Canals, R. (2021) · Remote Sensing · doi:10.3390/rs13132486 · Cited by 264
Metadata
- Authors
- Lowe, A., Harrison, N., & French, A. P.
- Venue
- Plant Methods
- Published
- 2017-10-10
- Volume
- 13
- Issue
- 1
- Pages
- 80-80
- ISSN
- 1746-4811 (ISSN-L)
- OpenAlex
- W2761176843
- Cited by
- 654
- References
- 32
- FWCI
- 31.32
- Type
- journal article
- Language
- en
- Open access
- gold
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- Lowe, A., Harrison, N., & French, A. P. (2017). Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods. https://doi.org/10.1186/s13007-017-0233-z
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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).
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