NSF Org:	CNS Division Of Computer and Network Systems
Recipient:	COLORADO STATE UNIVERSITY
Initial Amendment Date:	July 24, 2023
Latest Amendment Date:	May 3, 2024
Award Number:	2312319
Award Instrument:	Standard Grant
Program Manager:	David Corman dcorman@nsf.gov  (703)292-8754 CNS  Division Of Computer and Network Systems CSE  Direct For Computer & Info Scie & Enginr
Start Date:	August 1, 2023
End Date:	July 31, 2026 (Estimated)
Total Intended Award Amount:	$1,199,846.00
Total Awarded Amount to Date:	$1,219,846.00
Funds Obligated to Date:	FY 2023 = $1,199,846.00 FY 2024 = $20,000.00
History of Investigator:	Sangmi Pallickara (Principal Investigator) sangmi@cs.colostate.edu Jay Breidt (Co-Principal Investigator) Jeffrey Niemann (Co-Principal Investigator) Shrideep Pallickara (Co-Principal Investigator) Allan Andales (Co-Principal Investigator)
Recipient Sponsored Research Office:	Colorado State University 601 S HOWES ST FORT COLLINS CO  US  80521-2807 (970)491-6355
Sponsor Congressional District:	02
Primary Place of Performance:	Colorado State University 200 W. Lake St. FORT COLLINS CO  US  80521-4593
Primary Place of Performance Congressional District:	02
Unique Entity Identifier (UEI):	LT9CXX8L19G1
Parent UEI:
NSF Program(s):	Special Projects - CNS, CPS-Cyber-Physical Systems
Primary Program Source:	01002324DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7924, 9251
Program Element Code(s):	171400, 791800
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

We all depend on agriculture for sustenance. When compared to seafood and livestock, cropping systems provide the primary source of nutrition. Yields and productivity of cropping systems must grow to meet the demands of a growing population. Once seeds are available, a successful cropping season is determined by water. There are two sources for this: irrigation and precipitation. Irrigation water is a major input to agriculture, especially in semi-arid and arid regions. In a recent appraisal for the Soil and Water Resources Conservation Act, the USDA identified irrigation water conservation as a national need. Under-watering induces stresses and adversely impacts both crop growth and yields. Over-watering, on the other hand, leads to nutrient runoff, soil erosion, and water waste. Farms are also impacted by the adverse effects of droughts, variability in precipitation, and lengthening of the growing season. The proposed effort with its emphasis on water management and conservation represents an adaptation to the head winds often encountered at farms. The effort addresses the interrelated aspects of over-watering (soil erosion and nutrient runoff) and underwatering (adverse crop yields and stress) while ensuring sustainability and profitability of agricultural systems.

The overarching objective of this project is to develop an end-to-end cyber-physical intelligence system that forecasts space-time crop water needs in a given field and implements variable rate irrigation strategies to optimize crop yield throughout the field. We instrument the field with a limited number of in-situ soil moisture content sensors; these in situ observations are complemented with remotely sensed data from radars and satellites. The effort includes design of novel AI (Artificial Intelligence) methods based on deep neural networks (DNN) to generate forecasts of water needs. These DNNs operate on multimodal, high-dimensional data to identify soil moisture deficits and variability in different parts of the field. The generated forecasts account for crop, soil type, precipitation events, and the crop growing phase. The project closes the loop between the sensing environment and actuation within the AI-guided cyber physical system. These projections are leveraged within a game theory based algorithm to inform precise actuations of the watering arm with prescription plans that control watering rates at the nozzle and zone level. The algorithm is adaptive and responsive to precipitation events, uncertainty in the forecasts, and the actuation overheads. This multifaceted research advances the science of cyber-physical systems by innovatively combining sensing environments, algorithmic game theory, scientific models and domain-science, and AI/DNNs.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.