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Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards

Grapevine leafroll disease (GLD) is considered to be one of the most economically destructive viral diseases of grapevines worldwide, resulting in reduced vine vigor, yield reductions, and poor fruit quality. Five pathogens (grapevine leafroll-associated viruses) are recognized as causal agents of this disease, of which Grapevine leafroll-associated virus 3 (GLRaV-3) is the most common. Although there is no cure for the disease, management strategies (including vigilant removal of infected vines) can drastically limit its spread and economic impact. Comprehensive mapping (total population sampling) of disease presence on the ground is cumbersome and cost prohibitive in many situations. In addition, the practice of identifying diseased vines requires individuals to be highly trained to recognize symptoms. Compared to more traditional detection methods, airborne hyperspectral imaging offers a potentially valuable alternative for monitoring the disease that is cost effective, reliable and automatable. This study tests the use of hyperspectral imaging to aid in the management of GLD.

Over the span of two years we monitored five Cabernet Sauvignon vineyards: ground surveys recorded the incidence of visual symptoms of disease in the field during the same months in which hyperspectral maps recorded disease incidence from the air. A customized Geographic Information System (GIS) methodology was developed to compare the visual symptoms to the results of the hyperspectral imaging technique. For a select number of vines, disease incidence was then confirmed by laboratory assays. On average, detection sensitivity was 94.1%, with a range of 88% to greater than 99% per vineyard. Various vineyard-specific factors appear to compromise detection sensitivity. Overall, our results show that remote hyperspectral imaging of GLRaV-3 infected Cabernet Sauvignon vineyards can be a useful and cost-effective approach to mapping diseased vines. Future studies should focus on the use of this tool for detecting GLRaV-3 in other grape varieties, as well as other grapevine pathogens.

Authors:

Sarah L. MacDonald, Matthew Staid, Melissa Staid, Monica L. Cooper

Published in:

Computers and Electronics in Agriculture Volume 130, 15 November 2016, Pages 109-117

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