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

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.