SBIR Phase I: Machine Learning Driven Synthetic Sensor for Plant Water Stress

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1843254
Agency Tracking Number: 1843254
Amount: $225,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: CT
Solicitation Number: N/A
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-02-01
Award End Date (Contract End Date): 2019-09-30
Small Business Information
1925 Kenyon Street NW, Washington, DC, 20010
DUNS: 081044769
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Adam Koeppel
 (202) 664-1254
 adam@aquasys.co
Business Contact
 Adam Koeppel
Phone: (202) 664-1254
Email: adam@aquasys.co
Research Institution
N/A
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to increase yields and save irrigation water and costs for farmers of perennial crops and high intensity annual crops. In order to maximize yields while simultaneously minimizing irrigation water usage, farmers need to understand, predict, and manage plant water stress, which is the yield reducing stress plants undergo when they struggle to draw water from the soil. If successful, this project will demonstrate to farmers that low-cost measurement and forecasting of plant water stress is possible, and that Machine Learning (ML)-based recommendations for irrigation scheduling reduce water and energy usage, eliminate plant water stress, and increase crop yields. Research and Development activities completed will enhance the understanding of the relationship of plant water stress to microenvironmental conditions and how plant water stress and microenvironmental conditions can be accurately measured and forecast with low cost sensing capabilities. This SBIR Phase I project proposes to perform research and development to validate the concept of a low-cost synthetic sensor for plant water stress. The objective of this project is to field test the low-cost sensor arrays, compare the output to data from high-cost scientific grade sensors, and utilize the data gathered during the field test to train ML models. These ML models will perform sensor fusion on the data from the low-cost sensor arrays and output a plant water stress measurement. These ML models can then forecast future plant water stress, which will be compared to actual field measurements for accuracy assessment and refinement. Once refined, the ML models will determine the minimal amount of irrigation water necessary to mitigate the predicted plant water stress. This optimized amount of irrigation water will be allocated into a recommend irrigation schedule for farmers to review and implement. The relevance and ease of use of the recommended irrigation schedule will then be assessed by farmers. 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.

* Information listed above is at the time of submission. *

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