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.?