Better prediction of plant diseases

The interdisciplinary Plant Aid project developed advanced tools for detection and diagnosis in the field

PUBLISHED ON 29 August 2024

RALEIGH, NC — Each year, farmers around the world lose 20 to 40 percent of their crops to disease. To help farmers reduce those losses, researchers at North Carolina State University are using knowledge gained over the past four years to better predict crop disease epidemics.

The Plant Aid project 2020–23, funded by the University's Game-Changing Research Incentive Program for Plant Sciences Initiative (GRIP4PSI), developed and tested novel disease sensors, diagnostics and prediction tools for plant diseases.

William Neal Reynolds Professor Jean Ristaino of North Carolina State University's Department of Plant Pathology and Entomology led the project. She said the $556,250 GRIP4PSI grant is key to bringing together the kind of faculty expertise needed to help farmers reverse the rise in plant diseases.

These outbreaks have been exacerbated by climate change and new fungicide-resistant strains of plant pathogens that can be transmitted through global plant trade, she said.

Predecessor of Plant Aid

Ristaino: Better predictions of plant diseases enable farmers to take action before disease occurs, rather than having to wait until the disease is detected and diagnosed.

“In large plant disease pandemics, it is usually a case of everyone pitching in to solve the disease problem, find out where it is coming from and develop control strategies,” Ristaino said. “What we are doing now with our prediction tools is to work ahead of the epidemic: what signals might emerge that tell us that a disease outbreak is imminent?”

Over the past few decades, North Carolina State University has laid the groundwork to make such predictions possible, establishing centers such as the Center for Integrated Fungal Research and the Center for Geospatial Analytics and hiring new faculty—now five—as part of a new cluster on emerging plant diseases and global food security.

Ristaino said the Plant Aid project has given faculty members the impetus they need to work with others from different disciplines on important diseases that threaten agriculture in the United States and around the world.

“This allowed us to start brainstorming and developing tools and technologies for disease surveillance that we would not have been able to develop if we had worked independently,” Ristaino said. “We started to 'break down silos.'”

The development of the project

Initially, the interdisciplinary Plant Aid team focused on early blight, tomato spot virus, bacterial spot and late blight in tomato. The team developed sensors and rapid diagnostic tests that were subsequently evaluated for use in agricultural fields.

They also began working on a Plant Aid database with Chris Jones, geographer at the Center for Geospatial Analytics. Their goal: to better predict disease outbreaks by linking the sensors and tests with remote crop health sensor systems, genomic monitoring, real-time spatial and temporal data analysis, and climate data.

On the rise

The Plant Aid project has broken new ground in recent years as researchers have formed academic, industrial and international partnerships and applied the sensors and other tools to diseases of other crops, animals and even humans. They have also moved closer to real-time detection, diagnosis, prediction and prevention.

The preliminary data collected through GRIP4PSI were “directly incorporated into subsequent grants,” Ristaino said.

Over five years, Plant Aid researchers have received multimillion-dollar grants from the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service to develop additional monitoring and predictive tools. These funds supported work related to Phytophthora infestans, which leads to late blight in potatoes and tomatoes, And Phytophthora ramorum, an organism that causes tree diseases in the Western United States and spreads through disease in nursery crops.

In addition, a team led by Ristaino received a $1 million grant from the National Science Foundation's Predictive Intelligence for Pandemic Prevention (PIPP) program. This project supported the expansion of the Plant Aid database and provided additional knowledge to predict when and where plant diseases will emerge.

The grant enabled the researchers to validate the Plant Aid database with the pathogen that causes late blight and the pathogen that against downy mildew in cucurbits, an important disease of cucumbers, cantaloupe melons, watermelons, pumpkins and squashes.

A symposium was also held to examine how human attitudes and social behaviors affect plant disease transmission and the adoption of sensing, monitoring, and disease prediction technologies. This symposium, “Predicting the Next Plant Disease Pandemic Symposium,” helped lay the groundwork for subsequent partnerships and major funding opportunities.

Meanwhile, a team led by Qingshan Wei of North Carolina State University's Department of Chemical and Biomolecular Engineering leveraged previous work on sensor development to secure a $600,000 grant from the National Science Foundation's Convergence Accelerator to optimize the sensors to best meet the needs of growers and other users.

The devices, which detect volatile organic compounds, have been tested for diseases in tomatoes, potatoes and rhododendrons and could also be used to detect other plant diseases as well as in human and veterinary medicine.

Moving forward

Today, Plant Aid projects involve five North Carolina state colleges, as well as other universities, government agencies, international partners and the agricultural industry. Ristaino hopes the work will ultimately culminate in a center that will help farmers in North Carolina and elsewhere. Such a center would continue to develop and deploy new tools and a robust real-time prediction system for a wide range of plant diseases around the world.

“Currently, predicting crop disease pandemics is unreliable due to a lack of real-time detection, monitoring and data analysis to make decisions and prevent the spread of crop diseases,” says Ristaino. “We are now trying to shorten analysis to a few days or even just one day so that a farmer can make decisions that can improve global food security and their bottom line.”

NOTE: Want to learn more about Plant Aid? The Center for Geospatial Analytics has a new Web portal describing the teams' future goals and containing a list of researchers worldwide involved in research on predictive intelligence in plant diseases.

–Dee Shore, NC State University