Spectral characterisation of fungal diseases on Vitis vinifera leaves

Aims: The aims of this study were to (1) detect alterations in the reflectance spectra of vines with fungal diseases, (2) map these alterations, and (3) determine the best wavelengths which may be used as early indicators of fungal diseases in vines.

Methods and Results: Cabernet Sauvignon vines grown in pots and kept in a greenhouse were inoculated with the pathogens causing mildew, powdery mildew, black-foot and Petri disease. In early stages of disease development, reflectance measurements were performed using a FieldSpec 3 spectroradiometer, which were compared with data from healthy plants. Additional measurements were performed with chlorophyll meters. The investigation began with discriminant analysis, which revealed that symptomatic plants are indeed separated from the control ones. Reflectance spectra were therefore further investigated, looking for alterations on the shape of the spectra, characteristic of each disease. The disease descriptors were based on ratios between spectral features internal to a spectrum, a procedure which allowed the derivation of parameters intrinsic to each disease. A set of thresholds, defined as the intensity ratios of reflectance at selected wavelengths, was derived for the studied diseases. The selected wavelength ratios were 443/496, 443/573, 443/695, 443/1900, 496/573, 496/695, 516/1900, and 1900/2435 (values in nanometers), for which the spectra from symptomatic plants present shape changes of as much as 20% with respect to healthy plants.

Conclusions:

Spectral deformations were observed for the studied fungal diseases; they are larger for black-foot and powdery mildew, but some wavelength ratios are also indicators of downy mildew and Petri disease. Data from near-infrared in general carry more information compared with measurements at 1900 and 2435nm.

Significance and Impact of the Study: Since little is known on alterations of the reflectance spectra of vines, a better knowledge could be used in the development of sensors able to detect diseases through fast, non-destructive techniques. Early disease detection can lead to preventive actions which potentially can mitigate losses in grape yield and quality.