Research

One of the main focus areas of the program is the development of disease prediction models for economically important turfgrass diseases. We study the temporal and spatial disease progress and develop risk maps using machine learning algorithms. Our ultimate goal is to develop online forecasting systems available to the turfgrass industry. 

Genetic mapping studies including genome-wide association and biparental mapping are used to identify genomic regions associated with disease resistance. The goal is to incorporate these regions into breeding lines in future.

Molecular assays provide fast and accurate identification and quantification of plant pathogens. We develop and validate molecular assays such as quantitative PCR, recombinase polymerase amplification (RPA), and loop-mediated isothermal amplification (LAMP) for turfgrass pathogens. Some of these techniques including RPA and LAMP could be adapted to be used under field conditions which is our ultimate goal.  

One of our goals is to determine the prevalence of the nematode species present in golf courses and athletic fields through surveys. Further, we conduct research projects under greenhouse and field conditions to identify the most efficient management practices for common nematode-related diseases on turfgrass in Maryland. Please contact us if you have a history of nematode-related issues in your field/course/ lawn.

We use remote sensing and machine learning for the prediction of plant diseases and their identification. Two current studies focus on the application of RGB-Cam and hyperspectral imaging in the identification of turfgrass diseases.