Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

• Main Authors: Arachchige Surantha Ashan Salgadoe, Andrew James Robson, David William Lamb, Elizabeth Kathryn Dann, Christopher Searle
• Format: Article
• Language: English
• Published: MDPI AG 2018-02-01
• Series: Remote Sensing
• Subjects: avocado; canopy porosity; RGB image gap analysis; phytophthora root rot disease (PRR); vegetation indices worldview-3; multispectral satellite imagery
• Online Access: http://www.mdpi.com/2072-4292/10/2/226

Phytophthora root rot (PRR) infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, canopy decline, defoliation, and, eventually, tree mortality. Typically, the severity of PRR disease (proportion of canopy decline) is assessed by visually comparing the canopy health of infected trees to a standardised set of photographs and a corresponding disease rating. Although this visual method provides some indication of the spatial variability of PRR disease across orchards, the accuracy and repeatability of the ranking is influenced by the experience of the assessor, the visibility of tree canopies, and the timing of the assessment. This study evaluates two image analysis methods that may serve as surrogates to the visual assessment of canopy decline in large avocado orchards. A smartphone camera was used to collect red, green, and blue (RGB) colour images of individual trees with varying degrees of canopy decline, with the digital photographs then analysed to derive a canopy porosity percentage using a combination of ‘Canny edge detection’ and ‘Otsu’s’ methods. Coinciding with the on-ground measure of canopy porosity, the canopy reflectance characteristics of the sampled trees measured by high resolution Worldview-3 (WV-3) satellite imagery was also correlated against the observed disease severity rankings. Canopy porosity values (ranging from 20–70%) derived from RGB images were found to be significantly different for most disease rankings (p < 0.05) and correlated well (R2 = 0.89) with the differentiation of three disease severity levels identified to be optimal. From the WV-3 imagery, a multivariate stepwise regression of 18 structural and pigment-based vegetation indices found the simplified ratio vegetation index (SRVI) to be strongly correlated (R2 = 0.96) with the disease rankings of PRR disease severity, with the differentiation of four levels of severity found to be optimal.