Early detection is critical for the containment and eradication of devastating plant diseases, such as Xylella fastidiosa.
In 2018, scientists found that airborne hyperspectral and thermal imagery could reveal infection in olive trees months before the symptoms were visible.
The study obtained very high detection accuracies of trees showing Xylella fastidiosa infection symptoms. However, it relied on a manned airplane carrying sophisticated hyperspectral sensors that are rarely deployed operationally over large areas. As a result, this set-up is poorly suited for large-scale monitoring, using smaller aircraft, drones or other Unmanned Aerial Systems (UAS).
Multispectral cameras are more cost-efficient and user-friendly than most hyperspectral sensors. However, they typically measure no more ten spectral bands (compared to dozens in hyperspectral sensors), which generally limits their ability to pick up the plant physiological changes caused by early stages of disease infection.
The new paper shows that scientists can overcome this drawback by selecting the band-sets of multispectral cameras based on their sensitivity to the alteration of infected trees’ normal functions. More precisely, the study found that the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most ‘sensitive’ to Xylella fastidiosa symptoms in olive trees. The study points out that airborne platforms carrying multispectral and thermal cameras can support Xylella fastidiosa large-scale monitoring, if the band-set is carefully selected. According to the model presented in the study, the simplification lowers the detection accuracy of early infection symptoms by 6 to 10% compared to the more complex setup with a hyperspectral camera.
Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis.
https://doi.org/10.1016/j.isprsjprs.2020.02.010 | via Science Direct