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https://doi.org/10.21256/zhaw-3254| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor |
| Autor/-in: | Fahrentrapp, Johannes Ria, Francesco Geilhausen, Martin Panassiti, Bernd |
| DOI: | 10.21256/zhaw-3254 10.3389/fpls.2019.00628 |
| Erschienen in: | Frontiers in Plant Science |
| Band(Heft): | 10 |
| Heft: | 628 |
| Erscheinungsdatum: | 2019 |
| Verlag / Hrsg. Institution: | Frontiers Research Foundation |
| ISSN: | 1664-462X |
| Sprache: | Englisch |
| Fachgebiet (DDC): | 630: Landwirtschaft |
| Zusammenfassung: | Fungal leaf diseases cause economically important damage to crop plants. Protective treatments help producers to secure good quality crops. In contrast, curative treatments based on visually detectable symptoms are often riskier and less effective because diseased crop plants may develop disease symptoms too late for curative treatments. Therefore, early disease detection prior symptom development would allow an earlier, and therefore more effective, curative management of fungal diseases. Using a five-lens multispectral imager, spectral reflectance of green, blue, red, near infrared (NIR, 840 nm) and rededge (RE, 720 nm) was recorded in time-course experiments of detached tomato leaves inoculated with the fungus Botrytis cinerea and mock infection solution. Linear regression models demonstrate NIR and RE as the two most informative spectral data sets to differentiate pathogen- and mock-inoculated leaf regions of interest (ROI). Under controlled laboratory conditions, bands collecting NIR and RE irradiance showed a lower reflectance intensity of infected tomato leaf tissue when compared with mock-inoculated leaves. Blue and red channels collected higher intensity values in pathogen- than in mock-inoculated ROIs. The reflectance intensities of the green band were not distinguishable between pathogen- and mock infected ROIs. Predictions of linear regressions indicated that gray mold leaf infections could be identified at the earliest at 9 hours post infection (hpi) in the most informative bands NIR and RE. Re-analysis of the imagery taken with NIR and RE band allowed to classify infected tissue. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/17167 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Umwelt und Natürliche Ressourcen (IUNR) |
| Publiziert im Rahmen des ZHAW-Projekts: | Automated Airborne Pest Monitoring AAPM of Drosophila suzukii in Crops and Natural Habitats |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| Fahrentrapp-2019-Detection of Gray Mold Leaf I.pdf | 4.79 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Fahrentrapp, J., Ria, F., Geilhausen, M., & Panassiti, B. (2019). Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor. Frontiers in Plant Science, 10(628). https://doi.org/10.21256/zhaw-3254
Fahrentrapp, J. et al. (2019) ‘Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor’, Frontiers in Plant Science, 10(628). Available at: https://doi.org/10.21256/zhaw-3254.
J. Fahrentrapp, F. Ria, M. Geilhausen, and B. Panassiti, “Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor,” Frontiers in Plant Science, vol. 10, no. 628, 2019, doi: 10.21256/zhaw-3254.
FAHRENTRAPP, Johannes, Francesco RIA, Martin GEILHAUSEN und Bernd PANASSITI, 2019. Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor. Frontiers in Plant Science. 2019. Bd. 10, Nr. 628. DOI 10.21256/zhaw-3254
Fahrentrapp, Johannes, Francesco Ria, Martin Geilhausen, and Bernd Panassiti. 2019. “Detection of Gray Mold Leaf Infections prior to Visual Symptom Appearance Using a Five-Band Multispectral Sensor.” Frontiers in Plant Science 10 (628). https://doi.org/10.21256/zhaw-3254.
Fahrentrapp, Johannes, et al. “Detection of Gray Mold Leaf Infections prior to Visual Symptom Appearance Using a Five-Band Multispectral Sensor.” Frontiers in Plant Science, vol. 10, no. 628, 2019, https://doi.org/10.21256/zhaw-3254.
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