Virginia Tech researcher tackles corn disease with data
Ph.D. Student Kamal Chhetri integrates data-driven solutions and innovative technology to combat emerging foliar diseases, aiming to reduce corn crop losses.
Corn, the most widely grown crop in the United States, is increasingly affected by foliar diseases like tar spot, which can reduce yield and quality. Researchers at Virginia Tech’s Southern Piedmont Agricultural Research and Extension Center (SPAREC) are working on data-driven solutions to help producers monitor, detect, and manage these potentially devastating diseases.
Tar spot, caused by the fungal pathogen Phyllachora maydis, was first reported in the U.S. in 2015 and spread to Virginia in 2022, leading to significant yield and quality losses. Visible symptoms of the disease initially include flecking and mild chlorosis (yellowing) on leaves, stalks, and husks followed by the appearance of small, raised black spots – known as stromata.
Traditional fungicide treatment practices depend on calendar-based applications rather than real-time disease monitoring. This approach involves applying fungicides at specific growth stages of a crop without assessing the actual presence of foliar diseases. Consequently, this leads to higher fungicide application costs, environmental issues, and the potential development of fungicide resistance in pathogens.
Ph.D. student Kamal Chhetri joined the Zeng Lab at SPAREC in 2023 and has been making strides in sustainable disease management for crops with a particular focus on corn foliar disease epidemiology.
“My enthusiasm for plant pathology, technology, and data science inspired me to explore data-driven solutions by integrating pathogen monitoring with environmental data to improve current management practices in support of growers,” says Chhetri said.
Under the guidance of his advisors, plant pathologists Yuan Zeng and David Langston, Chhetri uses do-it-yourself, low-cost, solar-powered spore trap samplers and a high-throughput sequencing approach to monitor airborne fungal pathogens in corn fields. High-throughput sequencing enables scientists to quickly and efficient read the DNA sequences of numerous samples simultaneously.
The data obtained from airborne microbial samples captured by the spore traps have effectively detected key corn fungal pathogens before disease symptoms were observed in growers’ fields.
“The end goal is to develop an optimized fungicide application decision-support system for growers to better manage emerging and re-emerging foliar fungal diseases in corn and other economically important crops,” Chhetri said.
Funded by the Virginia Corn Board, Chhetri’s study holds significant potential for the development of more innovative tools for pathogen monitoring, early disease warning, and informed fungicide decision-making.
"This innovative approach empowers farmers to predict plant disease outbreaks and optimize fungicide application timing, advancing the Zeng lab’s mission to support sustainable crop production and enhance plant health in the face of climate change," said Plant Pathologist Yuan Zeng.
Since joining Virginia Tech, Chhetri has deepened his understanding of plant pathology and gained valuable experience in conducting and managing field trials. He has also acquired practical skills in wet and dry lab techniques, including DNA extraction from environment and plant samples, and DNA library preparation.
“By merging airborne pathogen data from solar-powered spore traps, weather patterns, and disease severity trends, I aim to generate machine-learning models that predict plant disease outbreaks,” says Chhetri. “Although focused on corn, the framework of my research can be applied to other crops.”
Chhetri hopes his work will at Virginia Tech will lead to more effective and sustainable disease management practices that will reduce crop losses and lower fungicide application rates, ultimately benefiting both farmers and Virginia’s agricultural productivity.
