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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.

Impacts of long‐term fertilization and cultivation were evaluated on nematode communities associated with tall fescue turfgrass following 11 years of treatment applications. Fertilizer treatments of biosolid, synthetic, and plant‐based fertilizers and cultivation treatments of 0×, 1×, and 2× aerification passes were applied to randomized and replicated tall fescue plots at the University of Maryland Paint Branch Turfgrass facility in College Park, Maryland. Free‐living and plant‐parasitic nematodes were identified, enumerated, and categorized into functional groups. Nematode count data were compared using generalized linear mixed modeling with negative binomial distribution and two‐way ANOVA was used to compare nematode ecological indices. Biosolid treatments resulted in lower omnivore‐predator densities than plant‐based fertilizer treatments (p ≤ .001) and significantly greater Hoplolaimus densities than plant‐based fertilizer plots (p ≤ .05). Synthetic fertilizer applications resulted in the greatest Eucephalobus (p ≤ .05) and total bacterivore densities (p ≤ .001) of all fertilizer treatments. Plant‐based fertilizer‐treated plots had the largest Maturity Index cp 2‐5 and Structure Index (p ≤ .05). Cultivation of 1× resulted in fewer total bacterivore densities than 2× (p ≤ .01) while omnivore‐predator densities were greater in 1× than 0× (p ≤ .001). Plant health, as measured by NDVI, was lowest in biosolid‐treated turfgrass (p ≤ .05). These findings suggest that long‐term turfgrass management practices can have variable impacts on nematode abundance and community structure in tall fescue and provide insights into ecological impacts of turfgrass management practices. Turfgrass fertilizer applications over 11 years had variable effects on soil nematode abundance and community structure. Biosolid‐based fertilizer applications where Cu, Fe, and Zn concentrations were highest resulted in the highest plant‐parasitic Hoplolaimus abundance and lowest beneficial omnivore‐predator nematode abundance.

Blackleg, caused by Leptosphaeria maculans, is a serious threat to canola (B. napus) production in North Dakota state that is its largest producer in the United States. Genome-wide association study (GWAS) was conducted on a set of 213 B. napus accessions inoculated with a mixture of five L. maculans isolates from pathogenicity group-four (PG-4) to identify genetic regions associated with resistance to this disease. Phenotypic data was obtained at the seedling stage using a 1–9 severity scale. This data was used to generate two binary (binary_3 and binary_5), and two polynomial (polynomial_median and polynomial_3) subsets. Using the median_severity phenotypic dataset (original) three significant markers were identified. By using the other four subsets five additional markers were detected. These eight significant markers (P < 0.00036) were distributed among chromosomes A1, A3, A6, A8, A9, C3, and C5. Two sets of three markers identified using the median_severity (original) and the polynomial_ median datasets, had the highest cumulative R2 values; they explained 36% and 34% of the phenotypic variation, respectively. A BLAST search within ±100 kb of these markers identified five genetic regions involved in the plant defense system. Information presented in this paper shows the benefit of using multiple arrangements of the same phenotypic dataset in GWAS. Furthermore, the markers and their allelic combinations identified in this study are valuable resources that could facilitate marker assisted selection to transfer blackleg resistance into modern breeding lines.

This dissertation contains three research chapters conducted on Sclerotinia stem rot (SSR) of canola (Brassica napus L.). This disease is caused by the fungus Sclerotinia sclerotiorum and is considered endemic in canola-producing areas of North Dakota. The first research chapter presents results of a study that evaluated the role of eight phenotyping scoring systems and nine variant calling and filtering methods in detection of QTL associated with response to SSR. The study, conducted on two doubled-haploid mapping populations, showed that using multiple phenotypic data sets derived from lesion length and plant mortality and imputing missing genotypic data increased the number of QTL detected without negatively affecting the effect (R2) of QTL. Nineteen QTL were detected on chromosomes A02, A07, A09, C01, and C03 in this study. The second research chapter presents results of a work that assessed the role of temperature regimes and wetness duration on S. sclerotiorum ascospore germination and ascosporic infection efficiency. This study showed that optimum ascospore germination occurred at 21 °C while it significantly decreased at 10 and 30 °C. Infection efficacy experiments indicated that extreme temperatures and interrupting wet periods were detrimental for the disease development. A logistic regression model with 75% accuracy was developed for the disease perdition. The third research chapter presents results of a study that evaluated the role of temperature on mycelial growth of 19 S. sclerotiorum isolates collected from different geographical regions and on SSR development on plant introduction (PI) lines with different levels of resistance. Mycelial growth and disease development peaked at 25 °C. While lesion expansion on resistant cultivars and the susceptible check was negatively affected at 30 °C, the disease developed significantly on the PI with a high level of susceptibility. Results of these studies provide insights into integrated management strategies of SSR.