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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor
Authors: Fahrentrapp, Johannes
Ria, Francesco
Geilhausen, Martin
Panassiti, Bernd
DOI: 10.21256/zhaw-3254
Published in: Frontiers in Plant Science
Volume(Issue): 10
Issue: 628
Issue Date: 2019
Publisher / Ed. Institution: Frontiers Research Foundation
ISSN: 1664-462X
Language: English
Subject (DDC): 630: Agriculture
Abstract: 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.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Natural Resource Sciences (IUNR)
Published as part of the ZHAW project: Automated Airborne Pest Monitoring AAPM of Drosophila suzukii in Crops and Natural Habitats
Appears in collections:Publikationen Life Sciences und Facility Management

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