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Fungal Plant Disease Detection

Fungal Plant Disease Detection

Fungal Plant Disease Detection

Review of mapping early-stage pathogen fungal colonisation through spectral imaging

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Pathogen fungal colonisation typically causes morphological changes in host plant tissue. Using a hyperspectral camera, a pixel-wise mapping of spectral reflectance in the visible and near-infrared range can be performed to describe an early-stage diseased tissue on the leaf or canopy level. Leaf structure anomalies are linked to leaf spectral reflectance patterns. Depending on the interaction with the host tissue, disease-specific spectral signatures are available.

The pixel-wise spectral extraction of plant signatures helps better understand leaf reflectance changes caused by plant disease dynamics. Proximal spectral imaging technology considerably improves the sensitivity and specificity of disease monitoring. In Fig. 1. it can be seen that much more information is available looking at a hot-spot or a pathogen location when using a hyperspectral imaging sensor (400-1000 nm).

Figure 1. Spatial Reflectance spectra of healthy and diseased leaves made by a hyperspectral camera (Mahlein et al. 2012)

Working with the spatial distribution of plant physiological information (reflectance) a new dimension of information content is opened and a novel disease management and monitoring tool is available. Some aspects might be important to consider.

  • Hyperspectral imaging detects patterns in time and space to identify disease or stress on the canopy level and on the tissue level.
  • Better understanding of plant optical properties during pathogenesis for early warning
  • Analysis methods and sensor experiences can be transferred and generalized for different plant-pathogen systems (spectral library of diseases)
  • It is a precise screening system for plant diseases and fungicide development.
  • Hyperspectral imaging is highly suitable to detect, identify and quantify fungal diseases on the leaf level.

Recent fungal pathogen detection user experiences using Cubert hyperspectral cameras (400-1000 nm)

  • Yellow rust (Puccinia striiformis f. sp. Tritici) detection in winter wheat (Ren et al. 2021)
  • Potato late blight (Phytophthora infestans) disease detection (Shi et al. 2021)
  • Ganoderma boninense Infection mapping of oil palm seedlings (Khairunniza-Bejo et al. 2021)
  • Fusarium Head Blight (Fusarium graminearum) mapping in winter wheat (Ma et al. 2021)
  • Sheath blight disease (Rhizoctonia solani) diagnosis on rice stalk (Zhang at al. 2021)


Khairunniza-Bejo, S., Shahibullah, M. S., Azmi, A. N. N., & Jahari, M. (2021). Non-Destructive Detection of Asymptomatic Ganoderma boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine. Applied Sciences, 11(22), 10878.

Ma, H., Huang, W., Dong, Y., Liu, L., & Guo, A. (2021). Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight. Remote Sensing, 13(15), 3024.

Mahlein, Anne-Katrin, Ulrike Steiner, Christian Hillnhütter, Heinz-Wilhelm Dehne, and Erich-Christian Oerke. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant methods 8, no. 1 (2012): 1-13.

Ren, Y., Huang, W., Ye, H., Zhou, X., Ma, H., Dong, Y., Shi, Y., Geng, Y., Huang, Y., Jiao, Q. and Xie, Q., 2021. Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data. International Journal of Applied Earth Observation and Geoinformation, 102, p.102384.

Shi, Y., Han, L., Kleerekoper, A., Chang, S., & Hu, T. (2021). A Novel CropdocNet for Automated Potato Late Blight Disease Detection from the Unmanned Aerial Vehicle-based Hyperspectral Imagery. arXiv preprint arXiv:2107.13277.

Zhang, J., Tian, Y., Yan, L., Wang, B., Wang, L., Xu, J., & Wu, K. (2021). Diagnosing the symptoms of sheath blight disease on rice stalk with an in-situ hyperspectral imaging technique. Biosystems Engineering, 209, 94-105.

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