Recipient Organization
NORTH CAROLINA STATE UNIV
(N/A)
RALEIGH,NC 27695
Performing Department
(N/A)
Non Technical Summary
Downy mildew (DM) is a devastating plant disease that spreads quickly and causes major losses to important crops like cucumbers, cantaloupes, and pumpkins. Farmers often cannot detect the disease early enough to stop it, leading to damaged plants, lower harvests, and economic losses. This project's goal is to develop a system that helps farmers detect DM early in their fields before the disease spreads. To achieve this, we will create a portable test for identifying DM spores using a new, rapid detection technology and design a robotic system that can automatically collect samples in the field. These tools will be tested on cucurbit crops (like cucumbers and squash) to ensure they work effectively. By detecting DM before it damages crops, farmers will know when and where to spray fungicides, reducing the number of sprays needed, improving crop yields, and saving money. This research will help farmers better manage DM, protect valuable crops, and reduce the economic and environmental costs of this disease, ultimately improving food production and sustainability.
Animal Health Component
100%
Research Effort Categories
Basic
0%
Applied
100%
Developmental
0%
Goals / Objectives
The overarching goal of this project is to enhance the U.S. capacity for early detection and response to downy mildew (DM) threats in agriculture. DM outbreaks can cause severe crop damage, leading to reduced yields and unmarketable products. This project will develop a fully automated biosurveillance system, integrating robotics and rapid molecular diagnostics, to protect specialty crops valued at $7.5 billion annually. While focusing on cucurbits, the system will be adaptable to other DM-susceptible crops like spinach, lettuce, and basil. Objectives include: (1) Develop a chip-miniaturized CDM-CRISPR diagnostic system for rapid in-field detection of DM pathogens. (2) Design and deploy a robot for autonomous spore sampling and train future professionals in biosurveillance technologies. (3) Validate and extend the system through field testing to improve disease management practices and adoption by growers.
Project Methods
The project will employ innovative scientific methods and unique technologies to achieve its objectives. For Objective 1, we will develop a chip-miniaturized CDM-CRISPR diagnostic system for rapid in-field detection of downy mildew (DM). This will involve designing and optimizing isothermal CDM-CRISPR assays to enable precise detection of DM spores. The system will then be miniaturized to ensure portability and robustness for field-based molecular analysis, addressing the need for on-site, rapid diagnostics. For Objective 2, a multifunctional robot will be designed to achieve adaptive perception, locomotion, and efficient operation in constrained environments, such as cucumber fields. The robot will feature an intelligent perception system that generates a traversability map to support task execution. It will carry vacuum traps with assays for spore collection and will be compared to traditional sampling approaches to evaluate its efficiency and accuracy in detecting DM spores. For Objective 3, we will validate the system's field-collected data using existing molecular diagnostic assays. Additionally, we will conduct extension and outreach activities to share pathogen-informed management strategies with growers, combining traditional monitoring methods with novel DM detection technologies. Efforts to deliver science-based knowledge include hands-on training for next-generation engineers and biological professionals through the development and deployment of the robot, field demonstrations, and outreach workshops for farmers and stakeholders. Evaluation will focus on key milestones, including system performance (e.g., detection sensitivity and sampling efficiency), successful robot deployment in the field, and farmer adoption of pathogen-informed management strategies. Quantitative indicators such as reduced fungicide applications, improved crop yields, and stakeholder participation in workshops will measure the project's impact on knowledge, actions, and agricultural practices.