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A.S. Nair - PRISM Center & School of Industrial Engineering, Purdue University, West Lafayette 47907, USA.
Y. Tao - Bio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA.
S.Y. Nof - PRISM Center & School of Industrial Engineering, Purdue University, West Lafayette 47907, USA

Networked telerobots are operated by humans through remote interactions and have found applications in hazardous and/or unstructured environments, such as outer space, underwater, telesurgery, manufacturing, and production. In precision agricultural robotics, target monitoring, recognition, and detection is a complex task, requiring expertise. Hence, they can be more efficiently performed by collaborative human-robot systems. A HUB is an online portal, a platform to create and share scientific and advanced computing tools. Multiple HUBs have been developed recently for scientific research objectives. HUB-CI is a tool developed by PRISM Center at Purdue University to enable cyber-augmented collaborative interactions over cyber-supported complex systems. The research reported here, sponsored in part by BARD, implements the HUB-CI model to improve the Collaborative Intelligence (CI) of an agricultural telerobotic system for early detection of anomalies in pepper plants grown in greenhouses. Specific CI tools developed for this purpose include: (1) spectral image segmentation for detecting and mapping to anomalies in growing pepper plants; (2) workflow/task administration protocols for managing/coordinating interactions between software, hardware, and human agents, engaged in the monitoring and detection, which would reliably lead to precise, responsive mitigation. These CI tools aim to minimize interactions’ conflicts and errors that may impede detection effectiveness, thus reducing crops’ quality. On-going lab and field experiments indicate that planned and optimized collaborative interactions with HUB-CI (as opposed to ad-hoc or fixed-structured interactions) yield significantly fewer errors and better detection. Hence, it improves system productivity of precise monitoring for healthy growth of pepper plants in greenhouses

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The hub-CI model for telerobotics in greenhouse monitoring
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A.S. Nair - PRISM Center & School of Industrial Engineering, Purdue University, West Lafayette 47907, USA.
Y. Tao - Bio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA.
S.Y. Nof - PRISM Center & School of Industrial Engineering, Purdue University, West Lafayette 47907, USA

The hub-CI model for telerobotics in greenhouse monitoring

Networked telerobots are operated by humans through remote interactions and have found applications in hazardous and/or unstructured environments, such as outer space, underwater, telesurgery, manufacturing, and production. In precision agricultural robotics, target monitoring, recognition, and detection is a complex task, requiring expertise. Hence, they can be more efficiently performed by collaborative human-robot systems. A HUB is an online portal, a platform to create and share scientific and advanced computing tools. Multiple HUBs have been developed recently for scientific research objectives. HUB-CI is a tool developed by PRISM Center at Purdue University to enable cyber-augmented collaborative interactions over cyber-supported complex systems. The research reported here, sponsored in part by BARD, implements the HUB-CI model to improve the Collaborative Intelligence (CI) of an agricultural telerobotic system for early detection of anomalies in pepper plants grown in greenhouses. Specific CI tools developed for this purpose include: (1) spectral image segmentation for detecting and mapping to anomalies in growing pepper plants; (2) workflow/task administration protocols for managing/coordinating interactions between software, hardware, and human agents, engaged in the monitoring and detection, which would reliably lead to precise, responsive mitigation. These CI tools aim to minimize interactions’ conflicts and errors that may impede detection effectiveness, thus reducing crops’ quality. On-going lab and field experiments indicate that planned and optimized collaborative interactions with HUB-CI (as opposed to ad-hoc or fixed-structured interactions) yield significantly fewer errors and better detection. Hence, it improves system productivity of precise monitoring for healthy growth of pepper plants in greenhouses

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