Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22990
Title: Quantifying the severity of phytophthora root rot disease in avocado trees using image analysis
Contributor(s): Salgadoe, Arachchige Surantha Ashan (author); Robson, Andrew  (author)orcid ; Lamb, David  (author); Dann, Elizabeth (author); Searle, Christopher (author)
Publication Date: 2018
Open Access: Yes
DOI: 10.3390/rs10020226Open Access Link
Handle Link: https://hdl.handle.net/1959.11/22990
Abstract: 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.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 10(2), p. 1-17
Publisher: MDPIAG
Place of Publication: Switzerland
ISSN: 2072-4292
Field of Research (FOR): 070104 Agricultural Spatial Analysis and Modelling
070603 Horticultural Crop Protection (Pests, Diseases and Weeds)
Socio-Economic Outcome Codes: 960403 Control of Animal Pests, Diseases and Exotic Species in Farmland, Arable Cropland and Permanent Cropland Environments
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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