Home Science NASA helps spot wine grape disease from skies above California

NASA helps spot wine grape disease from skies above California

Plant pathologist Katie Gold, an assistant professor at Cornell University, inspects diseased grapes in a field. Gold’s team used a JPL-developed instrument to detect infected crops from the air in one of California’s most important wine grape producing regions. Credit: Allison Usavage

In a fascinating case study, scientists have successfully detected an expensive infection in cabernet sauvignon grapevines before any visible symptoms appeared. This breakthrough discovery was made possible through the use of an airborne science instrument developed at NASA’s Jet Propulsion Laboratory. The instrument, known as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS-NG), can accurately identify the subtle signs of the grape disease GLRaV-3 (grapevine leafroll-associated virus complex 3), which causes significant crop damage. This groundbreaking remote sensing technique has the potential to revolutionize ground-based monitoring for grape diseases and other crops. The research findings have been published in the journals Phytopathology and Journal of Geophysical Research: Biogeosciences.

NASA helps spot wine grape disease from skies above California
A drone captured a grateful message written among grapevines by individuals with the wine industry who collaborated on the pathogen-spotting research in the Lodi, California, region. Credit: Aaron Lange/Lodi Winegrape Commission

Aerial Advantage and Groundbreaking Research

The study focused on detecting the viral disease GLRaV-3, which is primarily spread by insects and causes substantial damage to the grape industry. The traditional methods of scouting and testing for this disease are time-consuming and costly. The use of AVIRIS-NG and machine learning techniques provided a groundbreaking solution to identify infected grapevines from the air. The researchers observed approximately 11,000 acres of vineyards in Lodi, California, a major wine grape producer, and achieved an impressive 87% accuracy in differentiating infected and non-infected vines. This early detection method gives grape growers up to a year to intervene and potentially save their crops. The success of this study highlights the potential of remote sensing and the integration of air and space capabilities in supporting ground-based pathogen surveillance efforts for crops.

The researchers also emphasized the importance of sustainable farming practices, especially in the face of climate change. They believe that scalable solutions, such as remote sensing and machine learning, can empower growers to make data-driven and sustainable crop management decisions. The ultimate vision is to expand this research globally and apply it to a wide range of crop diseases and growers worldwide. The forthcoming NASA mission, Surface Biology and Geology (SBG), part of the Earth System Observatory, is expected to provide valuable data for agricultural decision-making on a global scale.

This groundbreaking study showcases the potential of utilizing advanced technologies and innovative methods to revolutionize crop disease detection and monitoring. The collaboration between NASA’s Jet Propulsion Laboratory, Cornell University, and the wine industry has opened up new possibilities for early detection and intervention in the agriculture sector. The research findings offer hope for more efficient and sustainable farming practices and highlight the significant role that technology can play in enhancing agricultural productivity.

More information:
Fernando Romero Galvan et al, Scalable early detection of grapevine virus infection with airborne imaging spectroscopy, Phytopathology (2023). DOI: 10.1094/PHYTO-01-23-0030-R

Gloire Rubambiza et al, Toward Cloud‐Native, Machine Learning Base Detection of Crop Disease With Imaging Spectroscopy, Journal of Geophysical Research: Biogeosciences (2023). DOI: 10.1029/2022JG007342

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NASA helps spot wine grape disease from skies above California (2023, August 7)
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