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.

Durum wheat (Triticum turgidum L. ssp. durum) is an important food crop worldwide. Modern breeding has yielded elite durum wheat cultivars with improved grain yield, end‐use quality, and disease resistance. In this study, we compared 150 breeding lines from the North Dakota State University (NDSU) durum wheat breeding program to 163 durum landraces using a large set of single nucleotide polymorphism (SNP) markers. We found that the genetic diversity of the NDSU durum breeding population was decreased by ∼60% relative to collection of 163 landraces. Selective sweep analysis identified several candidate regions that might have undergone breeding selection. Also in this study, over 260 worldwide durum wheat accessions were evaluated in drought environments of the northern Great Plains for 3 yr, a major growing area of durum wheat in the United States. One line showed significantly higher grain yield than the local elite cultivars. A number of lines showed significantly higher grain yield component traits like seeds per spike and thousand‐kernel weight. Those desirable lines may contain complementary favorable alleles and can possibly be used to improve grain yield under drought stress. Genome‐wide association mapping found no major quantitative trait loci (QTL) but a number of QTL with minor effect for grain yield, which could be explained by grain yield's complex genetic nature, the rare frequency of the novel complementary alleles, and the limited number of environments and replications for phenotypic evaluation in the study. Further QTL mapping using biparental populations derived from the identified desirable lines and elite cultivars may be able to identify the QTL related to grain yield, which can facilitate introgression of the complementary favorable alleles through marker‐assisted selection.

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Plant diseases introduce significant yield and quality losses to the food production industry, worldwide. Early identification of an epidemic could lead to more effective management of the disease and potentially reduce yield loss and limit excessive input costs. Image processing and deep learning techniques have shown promising results in distinguishing healthy and infected plants at early stages. In this paper, the potential of four convolutional neural network models, including Xception, Residual Networks (ResNet)50, EfficientNetB4, and MobileNet, in the detection of rust disease on three commercially important field crops was evaluated. A dataset of 857 positive and 907 negative samples captured in the field and greenhouse environments were used. Training and testing of the algorithms were conducted using 70% and 30% of the data, respectively where the performance of different optimizers and learning rates were tested. Results indicated that EfficientNetB4 model was the most accurate model (average accuracy = 94.29%) in the disease detection followed by ResNet50 (average accuracy = 93.52%). Adaptive moment estimation (Adam) optimizer and learning rate of 0.001 outperformed all other corresponding hyperparameters. The findings from this study provide insights into the development of tools and gadgets useful in the automated detection of rust disease required for precision spraying.