Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five machine learning (ML) models were evaluated in classification and regression modes for predicting disease establishment under different air temperatures and leaf wetness duration conditions. Model algorithms were trained and tested using 20-fold cross validation. Correspondence between predicted and observed values were measured using Cohen’s Kappa (classification) and Lin’s concordance coefficients (regression). The artificial neural network (ANN) algorithms had average accuracies ≥ 89% (classification) and R2 ≥ 88% (regression) on canola and dry bean and their correspondence agreements were ≥ 0.83, which is considered substantial to almost perfect. In contrast, logistic regression algorithms had accuracies of 88% for dry bean and 78% for canola; other models were similarly inconsistent. Implementation of ANN models in disease warning systems could help farmers with spraying decisions. At the same time, these models provide insights on temperature and leaf wetness requirements for development of S. sclerotiorum diseases in these crops. Results of this study show the potential of ML models as tools for epidemiological studies on other pathosystems.

The polyploid nature of canola (Brassica napus) represents a challenge for the accurate identification of single nucleotide polymorphisms (SNPs) and the detection of quantitative trait loci (QTL). In this study, combinations of eight phenotyping scoring systems and six SNP calling and filtering parameters were evaluated for their efficiency in detection of QTL associated with response to Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, in two doubled haploid (DH) canola mapping populations. Most QTL were detected in lesion length, relative areas under the disease progress curve (rAUDPC) for lesion length, and binomial-plant mortality data sets. Binomial data derived from lesion size were less efficient in QTL detection. Inclusion of additional phenotypic sets to the analysis increased the numbers of significant QTL by 2.3-fold; however, the continuous data sets were more efficient. Between two filtering parameters used to analyze genotyping by sequencing (GBS) data, imputation of missing data increased QTL detection in one population with a high level of missing data but not in the other. Inclusion of segregation-distorted SNPs increased QTL detection but did not impact their R2 values significantly. Twelve of the 16 detected QTL were on chromosomes A02 and C01, and the rest were on A07, A09, and C03. Marker A02-7594120, associated with a QTL on chromosome A02 was detected in both populations. Results of this study suggest the impact of genotypic variant calling and filtering parameters may be population dependent while deriving additional phenotyping scoring systems such as rAUDPC datasets and mortality binary may improve QTL detection efficiency.

The impact of wetness duration and incubation temperatures on Sclerotinia sclerotiorum ascospore germination and ascosporic infection efficiency were evaluated. Ascospore germination was optimal when incubated in continuous moisture (free water) at 21°C. Significantly lower germination was observed at 10 or 30°C. Interrupting ascospore wet incubation was detrimental for germination. In infection efficiency studies, dry bean and canola flowers were inoculated with dry ascospores and placed on leaves of dry bean and canola plants, respectively. Dry bean plants were incubated for 196 h at 18 to 20°C in alternating 8 to 16 h wet/12 to 24 h dry periods. Canola plants were incubated for 240 h at 10, 15, 20, 25, or 30°C in alternating 6 to 18 h wet/18 to 6 h dry periods. Interrupting wet incubation delayed symptom appearance and hindered development of the epidemics on both plant types. Logistic regression models estimated at 50% the probability of disease development on dry bean and canola plants when 68 and 48 h of wet incubation at 20°C accumulated in a period of 6 days, respectively. The canola model was validated using data from field trials. Results of these studies will contribute to develop more accurate warning models for diseases caused by S. sclerotiorum.

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