The quest to develop a rapid and accurate large-scale screening process for Xylella in at-risk crop species through hyperspectral image analysis is moving forward.

Speaking at the Plant Biosecurity Research Symposium, Professor Pablo Zarco-Tejada from the University of Melbourne explained that findings from the use of this technology to date on abiotic induced stress of various crops have shown that hyperspectral indicators are species specific and pathogen specific.

The technology has been able to successfully identify Xylella infections from Verticillium disease infections, in both almonds and olives in Europe.

However, the applicability of these finding to our own situation in Australia is still unquantified.

The University of Melbourne is one of 27 research partners worldwide now looking to build on the knowledge of this European research and understand its applicability to other countries and situations, including Australia.

Between now and 2026, this collaborative is focusing on a number of research questions, including:

  1. Are the algorithms developed in Europe transferrable to Australia’s crop types, climates and pathogens?
  2. Can hyperspectral technology and machine learning be used to identify biotic-induced stresses such as caused by different xylem-limited pathogens, from those causing visually confounding symptoms induced by water stress and other abiotic factors?
  3. What is the transferability of detection algorithms from one crop type to another?
  4. Could we apply the algorithms developed in Europe via currently available satellites?

This article provides an update to the e-news article we wrote in December 2021 titled New tech boosts Xylella detection accuracy, about the use of hyperspectral analysis in Europe to detect Xylella in almonds and olives.

This technology, in conjunction with machine learning, has been particularly effective as an area-wide surveillance tool to quantify changes in Xylella infection over time.