Increasing atmospheric CO levels have a variety of effects that can influence plant responses to microbial pathogens. However, these responses are varied, and it is challenging to predict how elevated CO (eCO) will affect a particular plant-pathogen interaction. We investigated how eCO may influence disease development and responses to diverse pathogens in the major oilseed crop, soybean.
View Article and Find Full Text PDFThe common rust disease of maize is caused by the obligate biotrophic fungus Puccinia sorghi. The maize Rp1-D allele imparts resistance against the P. sorghi IN2 isolate by initiating a defense response that includes a rapid localized programmed cell death process, the hypersensitive response (HR).
View Article and Find Full Text PDFComputer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify incidence on maize leaves utilizing U-Net convolutional neural network models.
View Article and Find Full Text PDFAgrobacterium-based inoculation approaches are widely used for introducing viral vectors into plant tissues. This study details a protocol for the injection of maize seedlings near meristematic tissue with Agrobacterium carrying a viral vector. Recombinant foxtail mosaic virus (FoMV) clones engineered for gene silencing and gene expression were used to optimize this method, and its use was expanded to include a recombinant sugarcane mosaic virus (SCMV) engineered for gene expression.
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