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Puwadol Oak Dusadeerungsikul - PRISM Center and School of Industrial Engineering, Purdue University, 315 N Grant Street, West Lafayette, IN, 47907-2023, USA.
Shimon Y. Nof - PRISM Center and School of Industrial Engineering, Purdue University, 315 N Grant Street, West Lafayette, IN, 47907-2023, USA.
Yang Tao - Bio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA.

In this article, a Collaborative Control Protocol for Robotic and Cyber-Physical System (CCP-CPS) is presented. CCP-CPS, which enables an application of robotics in a CPS system for smart and precision agriculture (PA), aims to monitor and identify stresses in greenhouse crops by a hyperspectral analysis method. Roles of CCP-CPS are to assign tasks to agents, identify and resolve conflicts and errors in the system, and enable a more effective collaboration and interaction among agents in CPS, compared to traditional non-collaborative and non-CPS approach. Collaborative Control Theory (CCT) is utilized for two system levels; protocol and agent level (CCP level), and CPS and environment level (CPS level). We use two case studies to test and validate our method with alternative approaches. The results from computer simulation show that 1) CCP-CPS can identify the highest number of greenhouse locations containing stresses, 2) CCP-CPS can utilize available resources (time) effectively, and 3) CCP-CPS can respond to an emergency stress situation faster and have more tolerance capability with system conflicts and errors, by having human integration and cyber-augmented greenhouse system, than other monitoring systems.

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Collaborative Control Protocol for Agricultural Cyber-physical System
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Puwadol Oak Dusadeerungsikul - PRISM Center and School of Industrial Engineering, Purdue University, 315 N Grant Street, West Lafayette, IN, 47907-2023, USA.
Shimon Y. Nof - PRISM Center and School of Industrial Engineering, Purdue University, 315 N Grant Street, West Lafayette, IN, 47907-2023, USA.
Yang Tao - Bio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA.

Collaborative Control Protocol for Agricultural Cyber-physical System

In this article, a Collaborative Control Protocol for Robotic and Cyber-Physical System (CCP-CPS) is presented. CCP-CPS, which enables an application of robotics in a CPS system for smart and precision agriculture (PA), aims to monitor and identify stresses in greenhouse crops by a hyperspectral analysis method. Roles of CCP-CPS are to assign tasks to agents, identify and resolve conflicts and errors in the system, and enable a more effective collaboration and interaction among agents in CPS, compared to traditional non-collaborative and non-CPS approach. Collaborative Control Theory (CCT) is utilized for two system levels; protocol and agent level (CCP level), and CPS and environment level (CPS level). We use two case studies to test and validate our method with alternative approaches. The results from computer simulation show that 1) CCP-CPS can identify the highest number of greenhouse locations containing stresses, 2) CCP-CPS can utilize available resources (time) effectively, and 3) CCP-CPS can respond to an emergency stress situation faster and have more tolerance capability with system conflicts and errors, by having human integration and cyber-augmented greenhouse system, than other monitoring systems.

Scientific Publication
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