Scalable early detection of grapevine virus infection with airborne imaging spectroscopy

Abstract

Viral diseases, including Grapevine Leafroll-associated Virus Complex 3 (GLRaV-3), cause $3 billion in damages and losses to the United States wine and grape industry annually. GLRaV-3 has a well-studied, year-long latent period in which vines are infectious but do not yet display visible symptoms, making it an ideal model pathosystem to evaluate the scalability of symptomatic and asymptomatic imaging spectroscopy-based disease detection. Plant disease causes physiological and chemical changes to occur locally and systemically throughout a plant, which imaging spectroscopy can detect both directly and indirectly. Reliable and scalable disease detection during the latent period would greatly reduce management costs, as current detection methods are entirely ground-based, labor-intensive, and expensive. Here, we use data collected in September 2020 by the NASA Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA. During September 2020 and 2021, industry collaborators scouted 317 acres of Vitis vinifera winegrapes for visible disease symptoms, and collected a subset for confirmation molecular testing at a commercial facility. Grapevines identified as visibly diseased in 2021 were assumed to have been latently infected (asymptomatic) during the September 2020 AVIRIS-NG data collection. We combined random forest with synthetic minority oversampling technique (SMOTE) to train multiple spectral models able to distinguish between non-infected (NI) and GLRaV-3-infected grapevines. We observed clear spectral differences that allowed for differentiation between NI and GLRaV-3 infected vines both pre- and post-symptomatically at 1m through 5m resolution. Our two best performing models had 87% accuracy (0.73 Kappa) distinguishing between NI and asymptomatic (aSy), and 85% accuracy (0.71 Kappa) distinguishing between NI and (aSy + symptomatic [Sy]) respectively. We hypothesize these spectral differences are linked to changes in overall plant physiology induced by disease, as visible foliar symptoms were restricted to the lower canopy. HighlightsO_LIAirborne imaging spectroscopy allows for scalable early-detection models of grapevine leafroll-associated virus complex 3 (GLRaV-3). C_LIO_LIRandom Forest based models trained with scouting ground data and imaging spectroscopy are accurate up to 5 meter but perform best at 3 meter spatial resolution. C_LIO_LIGLRaV-3 detection via imaging spectroscopy will not replace existing field scouting strategies or molecular testing but supplement by allowing for more strategic resource deployment to improve the overall financial, environmental, and societal sustainability of winegrape production. C_LI