Phytophthora root rot disease (PRR) is a major threat in avocado orchards, causing extensive production loss and tree death if left unmanaged. PRR infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, showing canopy decline, defoliation and, if not managed, tree mortality. Although the Australian avocado industry has implemented several preventative strategies in orchards for managing the disease spread, assessment of disease severity in orchards remains a challenge. Commercially, PRR severity can be assessed visually by a ‘Ciba-Geigy’ canopy health ranking method, where the degree of canopy decline exhibited by infected trees is compared to a series of calibration photos. A rating of 0 signifies a healthy canopy, whilst a rating of 10 indicates total leaf loss and tree death. This method is highly subjective, labour inefficient, non- scalable and generally only provides a positive diagnosis of infected trees once a high severity of decline has occurred. As an alternative, this study evaluated a range of remote sensing technologies that may offer a non-invasive surrogate to the visual ‘Ciba-Geigy’ method. Red, green and blue (R,G, B), multispectral and thermal imagery were acquired from a range of commercially available sensors to evaluate the performance against PRR-induced canopy decline (assessed using ‘Ciba-Geigy’ method) within a commercial avocado orchard. RGB images acquired with a smartphone mounted FLIR ONE camera were able to quantify the canopy decline via canopy porosity associated with PRR infection (R² =0.98, RMSE 0.32). Worldview-3 (WV-3) satellite imagery, more specifically the simple ratio vegetation index (SRVI), produced the highest coefficient of determination in quantifying canopy decline as defined by the ‘Ciba-Geigy’ method (R² =0.96, RMSE 0.38). A measure of stomatal conductance derived from the proximal measure of canopy temperature (by FLIR B250 hand held camera) from the sunlit and shaded side of tree was found to be strongly correlated with canopy porosity associated with PRR (R² > 0.91). Additionally, this study developed a new analytical ‘histogram method’ for segregating thermal data associated with canopy to that non- canopy related. By offering an alternative to the commonly used ‘wet’ and ‘dry’ reference panel method, this output offers significant benefit for the future automated processing of many thermal images, such as that required for large commercial orchards. This thermal procedure was also found to detect canopy decline pre-visually (at ‘Ciba-Geigy’ ranking ‘2’). This research has clearly demonstrated the potential of remote sensing imagery acquired from a range of sensors, as a useful surrogate for assessing PRR induced canopy decline in avocado orchards. These approaches can significantly improve the scalability and efficiency of PRR assessment under commercial production.