This project develops science-driven modeling and decision-making algorithms for precision agriculture, with associated cloud-based implementations. This project would increase the potential for more efficient agricultural systems through the development of more accurate and larger scale models, data collection, and ultimately a software infrastructure that farmers could use for real-time monitoring of their crops.
The project is derived from scientific questions in sustainable agriculture, sensor networks, decision sciences, data science, and machine learning. The primary innovations are applications of grid-based soil parameters using incomplete data, machine learning-based time series analysis, and optimization problems to estimate the optimal mix of inputs for nutrients and irrigation. These applications are likely to have wide usage among the precision agriculture community. The cyberinfrastructure, MyGeoHub, is a cloud-based service that users can access through web browsers and leverages an existing infrastructure.
This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Established Program to Stimulate Competitive Research (EPSCoR).
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.
