CPS: Frontier: Collaborative Research: COALESCE: COntext Aware LEarning for Sustainable CybEr-Agricultural Systems

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One of the grand technical challenges of our generation is to get ready to feed 9 billion people by 2050 with sustainable use of water and chemicals. However, we are facing unprecedented challenges in adopting sustainable agricultural management practices, increasing production, keeping agriculture profitable and coping with deadly biotic and abiotic stresses and diseases as well as changing climate that threaten yield. This project aims to transform Cyber-Physical System (CPS) capabilities in agriculture to enable farmers to respond to crop stressors with lower cost, greater agility, and significantly lower environmental impact than current practices. The objective is to make foundational advances in AI, machine learning and robotics to individual plant-level sensing, modeling and reasoning. This enables small autonomous dexterous robots instead of the heavy farm equipment to monitor plants or small plots individually and treat them with minimum amount of chemicals. This also lowers the barrier to entry for small scale farmers, increases safety, minimizes runoff as well as soil compaction. This project includes a significant collaboration with the University of Illinois at Urbana-Champaign that is funded by the National Institute of Food and Agriculture (NIFA) within the U.S. Department of Agriculture.The research investigates multiple areas in data-driven estimation, control, and adaptation of complex cyber-physical systems, such as: (1) rigorous incorporation of domain knowledge and physical principles into a machine learning (ML)-driven estimation/prediction/control framework, (2) cross-modal information fusion for assimilating heterogeneous data streams that differ in type (categorical, discrete, or continuous), quality/accuracy/noise, and sampling frequency. (3) robust ML under a degraded sensing environment, (4) data-driven supervisory decision-making under resource constraints, such as data amount, data quality, privacy, and cost, (5) distributed control and coordination of autonomous teams of robots operating in harsh, changing, and uncertain field environments with partial observability, and (6) soft robotic arms and manipulators, along with embedded control and sensing systems, for agricultural manipulation by small mobile robots. The broader acceptance of the framework is facilitated by the team's unique collaboration with producer groups with direct connections to farmers. A wide range of knowledge dissemination plans target the CPS community, the farming community, and the general public. Education and outreach plans focus on the farming population and the next-generation scientific workforce. Specific activities and programs at the participating institutions are designed to broaden participation of Native American, Hispanic, African-American, and female students in computing and engineering. All research products and educational material generated by the project are being made publicly available through the project webpage.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.