Mycorrhizae play a pivotal role in multiple ecosystem processes, with far-reaching consequences for global Earth system processes. Recent discoveries have linked differences in mycorrhizal type to carbon and nutrient cycling and ecosystem sensitivity to CO 2 fertilization (

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.

The US wine and grape industry suffers $3B in damages and losses annually due to viral diseases such as Grapevine Leafroll-associated Virus Complex 3 (GLRaV-3). Current detection methods are labor intensive and expensive. GLRaV-3 undergoes a latent period in which the vines are infected but do not yet display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. We deployed the NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) 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 imagery acquisition. In both Sept. 2020 and 2021, industry collaborators scouted 317ac on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Grapevines identified as visibly diseased in 2021, but not 2020, were assumed to have been latently infected at time of acquisition. We trained spectral models with random forest and synthetic minority oversampling technique to distinguish non-infected and GLRaV-3-infected grapevines. Non-infected and GLRaV-3 infected vines could be differentiated both pre- and post-symptomatically at 1m through 5m resolution. The best-performing models had 87% accuracy distinguishing between non-infected and asymptomatic vines, and 85% accuracy distinguishing between non-infected and asymptomatic + symptomatic vines. The importance of non-visible wavelengths suggests this capacity is driven by disease-induced changes to overall plant physiology. Our work sets a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring.

Developing actionable early detection and warning systems for agricultural stakeholders is crucial to reduce the annual $200B USD losses and environmental impacts associated with crop diseases. Agricultural stakeholders primarily rely on labor-intensive, expensive scouting and molecular testing to detect disease. Spectroscopic imagery (SI) can improve plant disease management by offering decision-makers accurate risk maps derived from Machine Learning (ML) models. However, training and deploying ML requires significant computation and storage capabilities. This challenge will become even greater as global scale data from the forthcoming Surface Biology & Geology (SBG) satellite becomes available. This work presents a cloud-hosted architecture to streamline plant disease detection with SI from NASA's AVIRIS-NG platform, using grapevine leafroll associated virus complex 3 (GLRaV-3) as a model system. Here, we showcase a pipeline for processing SI to produce plant disease detection models and demonstrate that the underlying principles of a cloud-based disease detection system easily accommodate model improvements and shifting data modalities. Our goal is to make the insights derived from SI available to agricultural stakeholders via a platform designed with their needs and values in mind. The key outcome of this work is an innovative, responsive system foundation that can empower agricultural stakeholders to make data-driven plant disease management decisions, while serving as a framework for others pursuing use-inspired application development for agriculture to follow that ensures social impact and reproducibility while preserving stakeholder privacy.

Developing actionable early detection and warning systems for agricultural stakeholders is crucial to reduce the annual \$200B USD losses and environmental impacts associated with crop diseases. Agricultural stakeholders primarily rely on labor-intensive, expensive scouting and molecular testing to detect disease. Spectroscopic imagery (SI) can improve plant disease management by offering decision-makers accurate risk maps derived from Machine Learning (ML) models. However, training and deploying ML requires significant computation and storage capabilities. This challenge will become even greater as global scale data from the forthcoming Surface Biology \& Geology (SBG) satellite becomes available. This work presents a cloud-hosted architecture to streamline plant disease detection with SI from NASA’s AVIRIS-NG platform, using grapevine leafroll associated virus complex 3 (GLRaV-3) as a model system. Here, we showcase a pipeline for processing SI to produce plant disease detection models and demonstrate that the underlying principles of a cloud-based disease detection system easily accommodate model improvements and shifting data modalities. Our goal is to make the insights derived from SI available to agricultural stakeholders via a platform designed with their needs and values in mind. The key outcome of this work is an innovative, responsive system foundation that can empower agricultural stakeholders to make data-driven plant disease management decisions, while serving as a framework for others pursuing use-inspired application development for agriculture to follow that ensures social impact and reproducibility while preserving stakeholder privacy.