Galvan Fernando E Romero, Pavlick Ryan, Trolley Graham, Aggarwal Somil, Sousa Daniel, Starr Charles, Forrestel Elisabeth, Bolton Stephanie, Alsina Maria Del Mar, Dokoozlian Nick, Gold Kaitlin M
Cornell University, Cornell AgriTech, Geneva, NY 14456.
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109.
Phytopathology. 2023 Aug;113(8):1439-1446. doi: 10.1094/PHYTO-01-23-0030-R. Epub 2023 Sep 20.
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 + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
由于包括葡萄卷叶相关病毒复合体3（GLRaV-3）在内的病毒性疾病，美国葡萄酒和葡萄产业每年损失30亿美元。目前的检测方法既耗费人力又成本高昂。GLRaV-3有一个潜伏期，在此期间葡萄藤已被感染但未表现出明显症状，这使其成为评估基于成像光谱的疾病检测可扩展性的理想模型。2020年9月，美国国家航空航天局下一代机载可见和红外成像光谱仪被部署到加利福尼亚州洛迪市的赤霞珠葡萄园中检测GLRaV-3。图像采集后不久，作为机械采收的一部分，葡萄藤的叶子被摘除。在2020年和2021年9月，行业合作伙伴逐株巡查了317公顷葡萄园，寻找可见的病毒症状，并采集了一部分样本进行分子确认检测。2021年发现的有症状葡萄藤被假定在图像采集时已被潜伏感染。随机森林模型是基于未感染和感染GLRaV-3的葡萄藤的光谱信号进行训练的，通过对未感染和感染GLRaV-3的葡萄藤进行合成少数类过采样来平衡数据。这些模型能够在1至5米的分辨率下，在症状出现前和出现后区分未感染和感染GLRaV-3的葡萄藤。表现最佳的模型区分未感染和无症状葡萄藤的准确率为87%，区分未感染和无症状+有症状葡萄藤的准确率为85%。不可见波长的重要性表明，这种能力是由疾病引起的植物生理变化驱动的。这些结果为利用即将推出的高光谱卫星“表面生物学和地质学”进行葡萄藤及其他作物品种的区域疾病监测奠定了基础。[公式：见原文] 版权所有© 2023作者。这是一篇根据知识共享署名 - 非商业性使用 - 禁止演绎4.0国际许可协议分发的开放获取文章。
