Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy

作者：Fernando Romero Galvan, Ryan Pavlick, Graham Trolley, Somil Aggarwal, Daniel Sousa, Charles G. Starr, Elisabeth J. Forrestel, S. Bolton, María Mar Alsina, Nick Dokoozlian, Kaitlin M. Gold · 发表于：Phytopathology · 年份：2023 · DOI：10.1094/phyto-01-23-0030-r · 被引用次数：23 · 研究领域：Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses、Plant Virus Research Studies

The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + sympt...