The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping
Open accessAbstract
Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.
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Citations
Cites
Bergsträsser, S., Fanourakis, D., Schmittgen, S., Cendrero-Mateo, M., Jansen, M., Scharr, H., & Rascher, U. (2015) · Plant Methods · doi:10.1186/s13007-015-0043-0 · Cited by 209
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Mishra, P., Asaari, M. S. M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2017.09.009 · Cited by 319
From Microbiome to Traits: Designing Synthetic Microbial Communities for Improved Crop Resiliency
de Souza, R. S. C., Armanhi, J. S. L., & Arruda, P. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.01179 · Cited by 281
Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245
Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants
Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., & Kersting, K. (2016) · Scientific Reports · doi:10.1038/srep22482 · Cited by 118
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Idrissi, M., Hussain, A., Barua, B., Osman, A., Abozariba, R., Aneiba, A., & Asyhari, T. (2022) · Sensors · doi:10.3390/s22155497 · Cited by 25
Metadata
- Authors
- Mahlein, A.-K.
- Venue
- Plant Disease
- Publisher
- Scientific Societies
- Published
- 2016-02
- Volume
- 100
- Issue
- 2
- Pages
- 241-251
- ISSN
- 0191-2917 (ISSN-L)
- 1943-7692
- OpenAlex
- W2103959917
- Cited by
- 1,363
- References
- 1
- FWCI
- 45.19
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Mahlein, A.-K. (2016). Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Disease. https://doi.org/10.1094/pdis-03-15-0340-fe
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