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Fusion of sensor data for the detection and differentiation of plant diseases in cucumber
Author(s) -
Berdugo C. A.,
Zito R.,
Paulus S.,
Mahlein A.K.
Publication year - 2014
Publication title -
plant pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.928
H-Index - 85
eISSN - 1365-3059
pISSN - 0032-0862
DOI - 10.1111/ppa.12219
Subject(s) - biology , powdery mildew , hyperspectral imaging , transpiration , photosynthesis , chlorophyll fluorescence , cucumber mosaic virus , chlorophyll , botany , horticulture , remote sensing , plant virus , virus , virology , geology
The development of plant diseases is associated with biophysical and biochemical changes in host plants. Various sensor methods have been used and assessed as alternative diagnostic tools under greenhouse conditions. Changes in photosynthetic activity, spectral reflectance and transpiration rate of diseased leaves, inoculated with Cucumber mosaic virus ( CMV ), Cucumber green mottle mosaic virus ( CGMMV ), and the powdery mildew fungus Sphaerotheca fuliginea were assessed by the use of non‐invasive sensors during disease development. Spatiotemporal changes in leaf temperature related to transpiration were visualized by digital infrared thermography. The maximum temperature difference within a leaf was an appropriate parameter to differentiate between healthy and diseased plants. The photosynthetic activity of healthy and diseased cucumber plants varied as measured by chlorophyll fluorescence and compared to the actual chlorophyll content. Hyperspectral imaging data were analysed using spectral vegetation indices. The results from this study confirm that each pathogen has a characteristic influence on the physiology and vitality of cucumber plants, which can be measured by a combination of non‐invasive sensors. Whereas thermography and chlorophyll fluorescence are unspecific indicators for plant diseases, hyperspectral imaging offers the potential for an identification of plant diseases. In a sensor data fusion approach, an early detection of each pathogen was possible by discriminant analysis. Although it still needs to be validated under real conditions, the combination of information from different sensors seems to be a promising tool.