The transplant industry is a major source of disease outbreaks and can be a key point for disease control in the agriculture supply chain. To reduce the contribution of transplant facilities to disease outbreaks, the major goal of this project is to develop a novel disease detection and control CPS (Cyber-Physical System) to provide closed-loop disease control at a very early stage. We envision a novel disease detection and pathogen identification system that combines computer vision and real-time gene sequencing to provide closed-loop disease detection, pathogen identification, and control to reduce pathogen spread. In objective 1, we will develop a robotic system which can constantly scout large production greenhouses to perform real-time disease detection by imaging followed by sampling of plants that may already be infected. In objective 2, we will develop a pathogen identification method using a high-throughput, mobile device whereby sample preparation, sequencing, and data analytics will all be automated. In objective 3, we will integrate the results from image-based disease detection and sequencing-based pathogen identification with temperature and humidity data from environmental sensors and develop a model to guide the implementation of disease control strategies.?
COLLABORATIVE RESEARCH: CPS: MEDIUM: EARLY STAGE PLANT DISEASE DETECTION VIA ROBOTIC SAMPLING AND ON SITE METAGENOMIC SEQUENCING
Objective
Investigators
Kantor, G.
Institution
Carnegie Mellon University
Start date
2021
End date
2024
Funding Source
Project number
PENW-2020-11361
Accession number
1025567
Categories