An easy, rapid and accurate method to quantify plant disease severity: application to phoma stem canker leaf spots

Abstract

International audienceAssessing plant disease severity and pathogen population size is central to epidemiological studies that help to devise disease control practices for crop protection. Among current methods, there is a trade-off between accuracy, defined as the closeness of the estimated value to the true value, and cost, defined as the consumption of resources that have to be spent in order to achieve the appropriate measurement. On the one hand, accurate methods based on counting lesion numbers per plant are time consuming. On the other hand, quick methods based on evaluations of diseased area, are adequate for varietal evaluation, but not sufficient for a quantitative ranking of numerous observations such as those required for an estimation of spore dispersal. A new method based on counting of leaf spots observed during 1 min in a delimitated one square meter area (lesions counted/m(2)/min, Mac.m2) was tested, using phoma stem canker as a case study in experimental plots and farmers' fields of oilseed rape. We showed that direct sampling of disease symptoms is feasible, reduces observation time and yields a continuous quantitative variable. We confirmed that: (i) lesions counted/m(2)/min (Mac.m2) values were correlated with mean number of leaf spots per plant (lesions/plant, Mac.pl); (ii) repeat phoma leaf spot counts of the same sample area by the two methods (Mac.m2 and Mac.pl) were correlated when assessed either by the same or different observers; and (iii) ranking of field plots with different disease severity was coherent among observers. We conclude that the lesions counted/m(2)/min (Mac.m2) method is equal in performance to the lesions/plant (Mac.pl) method, and was faster. Used with care, this method will facilitate studies requiring disease severity estimates, which were previously hampered by cost and/or tim

Spatio‐temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen Leptosphaeria maculans

“…Estimates of disease severity at the leaf spot stage (start of the epidemic) were obtained from counts of blackleg leaf spots on canola plants in 1 min from one square metre (lesions counted m −2  min −1 ; Bousset et al., 2016) on 15 July. For each field plot, three observers counted leaf spots on green leaves while moving at constant speed (2 m per min) sideways along the length of a delimited 0.5 × 2 m area.…”

Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

“…That is, the objective of the studies is to collect reliable data for testing purposes (so the need is to consider reliability and cost—selecting the number of specimens and replicate (or sub-samples) estimates per sample to ensure sufficient reliability or agreement, while minimizing cost). Bousset et al ( 2016 ) also pointed out that, with given limited resources, cost is an important consideration in plant pathological studies. Chiang et al ( 2016b ) presented the concepts applied in medical research (Giraudeau and Mary 2001 ; Shoukri et al 2003 ; Shoukri 2004 ) and concluded that developing an optimal experimental design in which the number of specimens (individual units sampled) and the number of replicates (sub-sample) estimates per specimen for a fixed total number of observations (total sample size for the treatments being compared) are chosen to maximize statistical power and efficiency.…”

Automated image processing to support the analysis of between-year transmission of Leptosphaeria maculans in field conditions

“…In that context, the necessary epidemic data are generally difficult, costly and time-consuming to acquire (Bousset et al, 2015(Bousset et al, , 2016. Furthermore, as the direct quantification of the pathogen (or inoculum) and the infectiousness status of hosts plants (Susceptible, Latent, Infectious, Removed) are still challenging for plant diseases, they are indirectly inferred from pathology status data (Leclerc et al, 2014), often insufficient for testing mechanistic models without resorting to timeconsuming Bayesian methods (Bousset et al, 2015).…”

“…Furthermore, as the direct quantification of the pathogen (or inoculum) and the infectiousness status of hosts plants (Susceptible, Latent, Infectious, Removed) are still challenging for plant diseases, they are indirectly inferred from pathology status data (Leclerc et al, 2014), often insufficient for testing mechanistic models without resorting to timeconsuming Bayesian methods (Bousset et al, 2015). In this context, developing high-throughput and high-precision phenotyping methods appears as a main challenge for plant disease pathology and epidemiology (Bousset et al, 2016;Simko et al, 2016). For instance, as already shown by several authors, image-based phenotyping can be useful for detecting and quantifying disease symptoms (Camargo and Smith, 2009;Mahlein, 2016) and automated image processing enables one to expand substantially the throughput of disease data (Karisto et al, 2018;Stewart et al, 2016) which may feed modelling approaches and support empirical studies.…”