Use of UAS and Hyperspectral Remote Sensing for Early detection of Phylloxera Infestation in Vineyards

Why it matters

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. However, there are still challenges in remote sensing applications, such as detecting early incursions of cryptic pest species such as grape phylloxera (Daktulosphaira vitifoliae Fitch) in vineyards. Grape phylloxera is currently present in most grape-growing countries, but relatively localised in wine districts in south-eastern Australia.


The focus of this research is to develop a novel methodology for collecting, processing, analysing and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners, reliable data collection protocols and methods to achieve faster processing and integrate multiple sources of data in diverse remote sensing applications.






The aim of this project is to use predictive models combined with high-resolution detection technologies to increase sampling efficiency and improve first detection rates.

The project objectives are:

  • Modelling region-wide environmental changes to identify criteria for selecting high-risk surveillance areas and compare these predictors to current selection methods deployed by biosecurity personnel;
  • Prioritise sampling times and areas within targeted areas to direct surveillance efforts and increase rate of first detection using higher-resolution surveillance technologies (fixed-wing UAVs) and unique spectral signatures;
  • Evaluate utility of higher-resolution cameras and robotic technologies on multi-rotor UASs to categorise and/or collect target pests on different plant structures for identification by trained diagnosticians; and
  • Synthesise modelling and improved UAS technologies to demonstrate a practical application for surveillance of high priority plant pests in commercial crops.

Real world impact

The project has so far achieved:

  • The development of a digital model to assess the vigour of vineyards based on ultra-high resolution airborne collected RGB imagery.
  • We explore and propose new vegetation indices based on multi and hyperspectral imagery to highlight Phylloxera infestation symptoms.
  • A study of the characteristics of spectral signatures for different levels of Phylloxera infestation in grapevines at two different seasons and finally we carried out a correlation study integrating vegetation indices, digital vigour model (DVM) outputs and ground based data such as visual vigour assessment and EM-38 soil conductivity to identify key relationships with the aim of developing a predictive model for Phylloxera infestation in vineyards.
  • This project developed a novel (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses multirotor UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors combined with ground and spatial data. The implemented methodology provides the necessary data for the development of a predictive model for grape phylloxera infestation detection.

Other Team Members

  • Dr Dmitry Bratanov
  • Dr John Weiss
  • Dr Kevin Powell