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Fusarium head blight (FHB or scab) is a fungal disease of small grains caused by a handful of species of the Fusarium graminearum species complex (FGSC;Aoki et al., 2012). In the temperate and subtropical regions of the Americas, including Brazil, F. graminearum sensu stricto (hereafter F. graminearum) is the main pathogen. FHB is a major concern not only because of yield losses, but also due to the presence of hazardous mycotoxins, particularly deoxynivalenol (DON), that poses a risk to animal and human health (Del Ponte et al., 2012;Duffeck et al., 2017).Disease control is best achieved through the use of fungicide sprays and less susceptible cultivars (Willyerd et al., 2012). To date,

Image analysis based on color thresholding is the reference method for measuring severity as percent area affected. It is deemed to produce accurate results, usually considered the "true" severity value. More than a dozen applications have been used for the task in phytopathometry studies, but none was coded in R language. Here we introduced and evaluated pliman, a suite for the analysis of plant images. In particular, we show functions for computing percent severity based on RGB information contained in image palettes prepared by the user. Six image collections, totaling 249 images, from different diseases (wheat tan spot, soybean rust, olive leaf spot, rice brown spot, bean angular spot, and Xyllela fastidiosa on tobacco) exhibiting a range of symptomatic patterns and severity were used to evaluate the agreement of pliman predictions with APS Assess, LeafDoctor and ImageJ. Three users independently prepared three image palettes (each representing leaf background, symptomatic or healthy leaf tissue) by manually inspecting and subsetting these target areas in the images. Pliman predictions by a joint palette (by joining images by the three users into one) were highly concordant (ρ c > 0.98) with measures by the other software for all but Xylella fastidiosa on Tobacco (ρ c = 0.49). The error for the latter may be due to the low contrast between symptomatic and healthy tobacco tissues. Users showed to be a source of variation in the overall concordance depending on the disease. Reduction in the image resolution (< 1 megapixel) did not impact the results. Combined with parallel processing, the use of low image resolution sped up the processing time resulting in pliman being ~170 to ~430 times faster than existing tools for disease quantification. Pliman showed great potential to produce accurate measures and accelerate studies involving plant disease severity measurements, especially for the batch processing of large sets of image collections.

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