As global population is expected to double by 2050, the need for securing adequate food production is becoming an urgent problem to be solved. At the same time agriculture‘s environmental footprint needs to be decreased drastically. Pathogens and pests are responsible for the loss of one third of global crop production, optimizing their management is of utmost importance. By combining information systems, sensors and enhanced machinery, the field of ‘precision agriculture’ promises to be a smart solution to fulfil the demands of modern agriculture. The site- and crop-specific adaption of precision agriculture can account for the variability and uncertainty in a managed landscape and thus allows for an improved use of resources, such as water, fertilizer or even pesticides and fungicides which in turn can help maintain environmental integrity. Remote sensing technologies, such as spectral sensors and spectral vegetation indices, are now routinely incorporated into precision agriculture strategies to monitor crop needs such as fertilizer, water and pathogen deterring agrochemicals across large areas.
In this project, the pathogen myrtle rust (Austropuccinia psidii) on lemon myrtle (Backhousia citriodora) was studied to explore whether it is possible to establish a remote sensing approach for the detection ‒ and management ‒ of myrtle rust in managed landscapes. Ground-based hyperspectral and aerial, multispectral sensors were utilized at leaf- and canopy-scale. We developed an innovative method to design a new form of spectral vegetation indices, a
disease-specific spectral vegetation index (SDI).
