2019-11-29, Salgadoe, Surantha, Lamb, David

Phytophthora root rot disease (PRR) is a major threat in avocado orchards. To identify a potential alternative to the current methods of assessing PRR in avocado for example visual assessment of canopy decline by human eyes, data has been collected from number of novel remote sensing technologies for measuring PRR induced canopy decline in avocado. Included Red Green and Blue (RGB) imagery acquired from a smartphone; thermal imagery from a hand-held camera and hyperspectral data acquired with a hand-held FieldSpec® 3 spectroradiometer

2020-04-07, Rahman, Moshiur, Robson, Andrew, Salgadoe, Surantha, Walsh, Kerry, Bristow, Mila

Accurate pre-harvest yield estimation of high value fruit tree crops provides a range of benefits to industry and growers. Currently, yield estimation in Avocado (Persea americana) and Mango (Mangifera indica) orchards is undertaken by a visual count of a limited number of trees. However, this method is labour intensive and can be highly inaccurate if the sampled trees are not representative of the spatial variability occurring across the orchard. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of yield. A stratified sampling technique was applied in each block to measure relevant yield parameters from eighteen sample trees representing high, medium and low vigour zones (6 from each) based on classified normalised difference vegetation index (NDVI) maps. For avocado crops, principal component analysis (PCA) and non-linear regression analysis were applied to 18 derived vegetation indices (VIs) to determine the index with the strongest relationship to the measured yield parameters. For mango, an integrated approach of geometric (tree crown area) and optical (spectral vegetation indices) data using artificial neural network (ANN) model produced more accurate predictions. The results demonstrate that accurate maps of yield variability and total orchard yield can be achieved from WV imagery and targeted sampling; whilst accurate maps of fruit size and the incidence of phytophthora can also be achieved in avocado. These outcomes offer improved forecasting than currently adopted practices and therefore offer great benefit to both the avocado and mango industries.

2020-05-06, Salgadoe, Surantha, Lamb, David, Robson, Andrew

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.