Measuring lesion attributes and analysing their spatial patterns at the leaf scale using digital image analysis

Abstract

Digital image analysis was used to quantify size, shape and relative positions of individual plant disease lesions to determine their spatial distribution pattern at the leaf scale. Rice brown spot was used as a necrotrophic pathogen causing numerous discrete lesions. A 50‐leaf subsample was selected from an existing data set of 350 images of leaves taken from the field, and analysed for disease severity using image analysis. Further measurements included size, shape and the relative positions of lesions for all leaves with severity > 8% (n = 25) and an additional 25‐leaf sample with severity <8%. A total of 3964 necrotic and/or halo areas were selected using a manually defined threshold in the computer program Assess. There were significant and positive associations (Pearson's r > 0.81; P < 0.001) between the size‐related measurements (lesion area, longest and shortest axis). Coalesced areas, formed by interconnection of lesions and associated haloes, and a high number of small lesions were found with an increase in severity, suggesting a secondary cycle and autoinfection process. Results from quadrat‐based (Poisson distribution and Spatial Analysis by Distance IndicEs) and distance‐based (point‐process Poisson) spatial methods were in good agreement and, together with a Taylor power law model, suggested a shift from random to predominantly aggregated patterns of lesions at severities approaching 10%. This framework, which is applicable to other foliar diseases, proved useful in providing quantitative knowledge of epidemic processes at the leaf scale. Finally, these results may be useful in improving simulation models and disease assessment methods.

Using R‐Based Image Analysis to Quantify Rusts on Perennial Ryegrass

“…However, obtaining FL images, which required harvesting leaf samples, positioning them, and capturing the image, took 140 s per plant. Schwanck and Del Ponte (2016) noted that leaf sampling and lesion counting is labor intensive and that visual estimates of severity may be faster. This was certainly the case in our study; however, gains in precision and accuracy for diseases such as stem rust at the cost of up‐front time expenses might be worthwhile for many researchers.…”

“…The remaining three had no experience rating rust but had routinely applied visual scales to plants in the field. A review of the literature shows that rater panels vary greatly in size, from three to 12 (Karcher and Richardson, 2003; Godoy et al, 2006; Schwanck and Del Ponte, 2016; Nuñez et al, 2017; Pedroso et al, 2011), thus we determined that nine would give an adequate representation of what could be commonly found within the plant sciences for rating rusts on a visual scale.…”

“…An alternative approach to repeated visual evaluation in the field is to collect digital images of leaves or plants and utilize a trained image analysis pipeline to quantify and analyze disease severity. Image analysis has already been shown to be an effective method to evaluate disease severity of several foliar pathogens including crown rust of oat ( Avena sativa L.) using Image Pro Plus software (Díaz‐Lago et al, 2003) and rice brown spot ( Bipolaris oryzae ) of rice ( Oryza sativa L.) using Assess software (Schwanck and Del Ponte, 2016). The potential advantages of utilizing digital photography to phenotype plant traits include improved accuracy, increased precision, more efficient use of labor, and the preservation of digital information for future analyses.…”

“…The resulting masked image can be further improved based on the spatial patterns of the feature being extracted. Morphological operations, adaptive thresholding, and segmentation are a few tools that previously have been implemented for plant cell counting (Sankar et al, 2014) but may also help determine disease severity on leaf or plant images (Schwanck and Del Ponte, 2016).…”

Combining Digital Imaging and Genome Wide Association Mapping to Dissect Uncharacterized Traits in Plant/Pathogen Interactions

“…For instance, 67 comparing the molecular and quantitative genetic basis of resistance using lesion size versus 68 the underlying biochemical plant resistance responses has shown that different genes can 69 contribute to these traits. This suggests that the lesion size measurements are not providing the 70 full picture of the resistance response (Rowe and Kliebenstein, 2008;Bock et al, 2010;Li et al, 71 2015;Corwin et al, 2016b;Schwanck and Del Ponte, 2016;Barbedo, 2017;Matsunaga et al, 72 2017). There has been recent interest in extending our understanding of plant-pathogen 73 resistance by conducting more extensive phenotyping of disease symptoms, including 74 hyperspectral imaging of lesions that records spectra from the visible into the infrared range 75 (Montes et al, 2007;Kuska et al, 2015;Mutka and Bart, 2015;Leucker et al, 2016).…”