Evaluating the Potential of High-Resolution Visible Remote Sensing to Detect Shiraz Disease in Grapevines

• Published in: Australian journal of grape and wine research 2023-05, Vol.2023, p.1-9
• Main Authors: Wang, Yeniu Mickey, Ostendorf, Bertram, Pagay, Vinay
• Format: Article
• Language: English
• ISSN: 1322-7130; 1755-0238
• DOI: 10.1155/2023/7376153

Background and Aims. Shiraz disease (SD) is a viral disease associated with Grapevine virus A that causes significant yield loss in economically important grape cultivars in Australia such as Shiraz and Merlot. Current diagnostic methods are time-consuming and costly. This study evaluates an alternative methodology using visible remote sensing imagery to detect SD in Shiraz grapevines. Methods and Results. High-resolution visible remote sensing images were captured of Shiraz grapevines in two South Australian viticultural regions over two seasons. The projected leaf area (PLA) of individual grapevines was estimated from the images. Virus-infected vines had significantly lower PLA than healthy vines in the early season but fewer difference after veraison. The lower PLA was only observed in grapevines coinfected with grapevine leafroll-associated viruses (GLRaVs) and Grapevine virus A (GVA). Shiraz vines infected with either GLRaVs or GVA had similar PLA to healthy vines. Conclusions. High-resolution RGB remote sensing technology has the potential to rapidly estimate SD infection in Shiraz grapevines. Our observations of shoot devigouration only in coinfected vines calls into question the etiology of SD. Further validation of the PLA technique incorporating different regions, seasons, cultivars, and combinations of viruses is needed for improving the robustness of the method. Significance of the Study. This preliminary study presents a new rapid and low-cost surveillance method to estimate SD infections in Shiraz vineyards, which could significantly lower the cost for growers who conduct on-ground SD visual assessments or lab-based tissue testing at the vineyard scale.