Using image analysis for quantitative assessment of needle bladder rust disease of Norway spruce |
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| Authors: | A Ganthaler A Losso S Mayr |
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| Affiliation: | Department of Botany, University Innsbruck, Innsbruck, Austria |
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| Abstract: | High elevation spruce forests of the European Alps are frequently infected by the needle rust Chrysomyxa rhododendri, a pathogen causing remarkable defoliation, reduced tree growth and limited rejuvenation. Exact quantification of the disease severity on different spatial scales is crucial for monitoring, management and resistance breeding activities. Based on the distinct yellow discolouration of attacked needles, it was investigated whether image analysis of digital photographs can be used to quantify disease severity and to improve phenotyping compared to conventional assessment in terms of time, effort and application range. The developed protocol for preprocessing and analysis of digital RGB images enabled identification of disease symptoms and healthy needle areas on images obtained in ground surveys (total number of analysed images n = 62) and by the use of a semiprofessional quadcopter (n = 13). Obtained disease severities correlated linearly with results obtained by manual counting of healthy and diseased needles for all approaches, including images of individual branches with natural background (R2 = 0.87) and with black background (R2 = 0.95), juvenile plants (R2 = 0.94), and top views and side views of entire tree crowns of adult trees (R2 = 0.98 and 0.88, respectively). Results underline that a well‐defined signal related to needle bladder rust symptoms of Norway spruce can be extracted from images recorded by standard digital cameras and using drones. The presented protocol enables precise and time‐efficient quantification of disease symptoms caused by C. rhododendri and provides several advantages compared to conventional assessment by manual counting or visual estimations. |
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| Keywords: | fungal pathogen phenotyping
Picea abies
plant disease subalpine unmanned aerial vehicle (UAV) |
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