Published in Nature Communications, our research with international partners from the European Union, United Kingdom, and the United States demonstrates that hyperspectral imaging and a novel algorithm can distinguish the disease from water-induced stress and increase Xf detection to up to 92% accuracy while reducing uncertainty to below 6% across different hosts. But a common problem is that the remote sensing algorithms that scan the hyperspectral images can’t always distinguish the symptoms of Xf from the symptoms of other pathogens or environmental stress like lack of water or nutrients. Our previous research has already demonstrated that we can use it to detect Xf in olive trees before symptoms were visible. Hyperspectral images allow us to “see” in more fine-grained wavelengths. And during this period, the asymptomatic plants continue to be infectious.īut new research takes us a step closer to developing a rapid and more accurate large-scale screening process of at-risk crop species by enhancing the effectiveness of airborne scanning that uses hyperspectral imaging. The key to containing Xf is early detection, which isn’t easy given that some infections don’t cause visual symptoms until eight to 10 months. Already widely distributed in the Americas, it has been identified in Spain, France, Israel, Iran and Taiwan. Xf is arguably the greatest disease threat to food security and agricultural productivity worldwide. Zarco-Tejada and Tomas Poblete with the University of Melbourne describe the Xf bacterium as “the number one biological security threat to Australian agriculture.” They report on their research into early detection of Xf in the following excerpts from a University of Melbourne article they wrote. Citrus variegated chlorosis is among the diseases that Xf causes.
Xylella fastidiosa ( Xf) bacterium causes incurable diseases that make plants wither and possibly die, scorching and browning leaves and reducing the size of fruit in a wide variety of important crops.