Assessing the Capacity of High-Resolution Commercial Satellite Imagery for Grapevine Downy Mildew Detection and Surveillance in New York State.

Grapevine downy mildew (GDM), caused by the oomycete , can cause 100% yield loss and vine death under conducive conditions. High-resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases such as GDM over large, heterogeneous fields. Here, we investigated the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance.

Non-Destructive Monitoring of Foliar Fungicide Efficacy with Hyperspectral Sensing in Grapevine.

Frequent fungicide applications are required to manage grapevine powdery mildew (). However, this practice is costly and has led to widespread fungicide resistance. A method of monitoring in-field fungicide efficacy could help growers maximize spray-interval length, thereby reducing costs and the rate of fungicide resistance emergence.

Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy.

The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3).

Effects of Nighttime Applications of Germicidal Ultraviolet Light Upon Powdery Mildew (), Downy Mildew (), and Sour Rot of Grapevine.

Nighttime applications of germicidal ultraviolet were evaluated as a means to suppress three diseases of grapevine. In laboratory studies, UV-C light (peak 254 nm, FWHM 5 nm) applied during darkness strongly inhibited the germination of conidia of , and at a dose of 200 J/m, germination was zero. Reciprocity of irradiance and duration of exposure with respect to conidial germination was confirmed for UV-C doses between 0 and 200 J/m applied at 4 or 400 s.

Deep semantic segmentation for the quantification of grape foliar diseases in the vineyard.

Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases.

Digital plant pathology: a foundation and guide to modern agriculture.

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements.

Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale.

Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology.

Plant pest surveillance: from satellites to molecules.

Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector.

A multi-omics approach to solving problems in plant disease ecology.

The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics.

Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning.

Understanding plant disease resistance is important in the integrated management of Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of disease detection that can identify infection before the onset of visual symptoms would improve management by greatly reducing disease potential and spread as well as improve both the financial and environmental sustainability of potato farms. In-vivo foliar spectroscopy offers the capacity to rapidly and non-destructively characterize plant physiological status, which can be used to detect the effects of necrotizing pathogens on plant condition prior to the appearance of visual symptoms.

Contact Reflectance Spectroscopy for Rapid, Accurate, and Nondestructive Clonal Lineage Discrimination.

Populations of , the oomycete causal agent of potato late blight in the United States, are predominantly asexual, and isolates are characterized by clonal lineage or asexual descendants of a single genotype. Current tools for clonal lineage identification are time consuming and require laboratory equipment. We previously found that foliar spectroscopy can be used for high-accuracy pre- and postsymptomatic detection of infections caused by clonal lineages US-08 and US-23.

Population Differentiation Within in North America.

, a biotrophic ascomycete in the order Diaporthales, causes eastern filbert blight (EFB) of hazelnuts ( spp.). Until recently, little has been documented on its genetic diversity and population structure.