GrapeSPEC: Advancing Plant Disease Sensing for Sustainable Agriculture
At the Grape Sensing, Pathology, and Extension Lab (GrapeSPEC) at Cornell University, we stand at the forefront of a new era in plant disease detection. Fueled by advancements in precision agriculture, Earth observation technologies, and artificial intelligence, our research aims to bridge the gap between scientific discovery and field application. Our vision is to enable rapid, scalable detection of plant diseases to inform early, effective management decisions that safeguard food systems and agricultural sustainability.
Our Mission
The Gold Lab studies the fundamental and applied science of plant disease sensing to improve early grape disease detection and management. Our overarching goal is to drive multidisciplinary innovation in sustainable disease management through collaborative research integrating sensing, modeling, and plant pathology. We develop, evaluate, and disseminate knowledge about new control mechanisms, tools, and techniques for grape disease management relevant to short- and long-term stakeholder needs. Our research goal is to derive foundational understanding, translational discovery, and applied evaluation in grape pathology and growing discipline of plant disease sensing with a clear path, even if long, towards stakeholder use and benefit. Our extension goal is to provide accessible grape disease management education and reliable early intervention decision support to New York and US industry stakeholders. We strive to coach the next generation of scientists to achieve their professional goals in a healthy, collaborative, encouraging, rigorous, and respectful environment.
What We Do
We combine plant pathology, remote sensing, and machine learning to advance early detection and targeted disease intervention. Traditional plant pathology emphasizes the disease triangle: pathogen, host, and environment. We add a fourth dimension—management—and focus on how these interactions play out in human-managed ecosystems. Our work seeks to answer one of agriculture’s greatest challenges: how do you manage a disease you can’t yet see?
Our team specializes in plant disease sensing across scales—from leaves to landscapes. We are:
- The only lab worldwide dedicated entirely to plant disease sensing
- One of four globally conducting this work, and the foremost in the U.S.
We leverage in situ and imaging spectroscopy (“hyperspectral imaging”) across:
- Laboratory-based phenotyping platforms
- Autonomous vineyard robots
- UAVs and satellite systems
This multi-scale approach allows us to track disease progression, quantify the impacts of management practices, and improve disease surveillance systems. Our work is supported by a diverse funding portfolio including NASA, USDA-NIFA, NSF, state agencies, commodity groups, internal Cornell grants, and agrichemical industry partnerships.
Core Research Areas
Our research spans fundamental to applied science and is organized around the following focal areas:
- Asymptomatic disease detection & surveillance
- Pathosystem distribution modeling
- Plant protection sensing (e.g., fungicide activity detection)
- Novel sensing tools & techniques
- Stakeholder-focused decision support systems
- Open-source sensing platforms & toolkits
- Applied grape disease management with biofungicides
Our work has been featured in more than 40 media outlets, including The Los Angeles Times, NASA HQ, Cornell Chronicle (Twice), Bloomberg News, Good Fruit Grower, and The Late Show with Steven Colbert.
Notable Research Highlights
- First report of asymptomatic viral disease detection via airborne remote sensing (Romero Galvan et al. 2023)
- First global maps of disease suitability using remote sensing data (Calderon et al., in prep)
- Discovery that fungicide activity can be remotely sensed (Gambhir et al. 2023)
- Development of a stakeholder-facing, cloud-based disease detection platform (Rubambiza & Romero Galvan et al. 2023)
- Co-development of a closed-loop robotic disease detection system (Liu et al., in prep)
The Future
Our research will continue to push boundaries in early detection, disease modeling, and precision management. We aim to:
- Build climate-resilient disease surveillance systems
- Improve outcomes for at-risk crops and producers
- Enable genotype-environment-management-specific diagnostics
Learn more about the field of plant disease sensing by reading our perspective in mSystems: Giving Plants a Voice