Abstract & Details
Description
Award ID: 2345854
The broader impact/commercial potential of this I-Corps project is that it is expected to increase food yield and water savings in contrast to soil-based growth. The automation function of the technology may also help resolve the labor shortage challenge associated with traditional farming. Sensor data could reduce pest control costs, which to date has been a major challenge associated with hydroponics farms. Automated hydroponics growing methods could improve the sustainability of agriculture and support both indoor and outdoor hydroponics growth systems, enabling farmers to grow a rich variety of crops more efficiently. This I-Corps project is based on a contactless miniature laser imaging method to detect precise hydroponics nutrient levels. The contactless sensor detects growth media reflection and scattering efficiency to characterize composition. The technology features integration of multiple sources of data for optimal growth control decision-making. Sensor data includes temperature, humidity, supplied water amount, light, plant stress, disease conditions and water level. This data is used to generate a smart plant growth model using a deep reinforcement learning method. A chemical-free, ultraviolet-C (UVC) panel will be used to monitor pest control, one of the key challenges of growing organic crops. It is expected that the technology may reduce labor costs by 40% and the utilization of resources (e.g. water, nutrition and electricity) by 15%. 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.
NSF Program Director: Ruth Shuman
The broader impact/commercial potential of this I-Corps project is that it is expected to increase food yield and water savings in contrast to soil-based growth. The automation function of the technology may also help resolve the labor shortage challenge associated with traditional farming. Sensor data could reduce pest control costs, which to date has been a major challenge associated with hydroponics farms. Automated hydroponics growing methods could improve the sustainability of agriculture and support both indoor and outdoor hydroponics growth systems, enabling farmers to grow a rich variety of crops more efficiently. This I-Corps project is based on a contactless miniature laser imaging method to detect precise hydroponics nutrient levels. The contactless sensor detects growth media reflection and scattering efficiency to characterize composition. The technology features integration of multiple sources of data for optimal growth control decision-making. Sensor data includes temperature, humidity, supplied water amount, light, plant stress, disease conditions and water level. This data is used to generate a smart plant growth model using a deep reinforcement learning method. A chemical-free, ultraviolet-C (UVC) panel will be used to monitor pest control, one of the key challenges of growing organic crops. It is expected that the technology may reduce labor costs by 40% and the utilization of resources (e.g. water, nutrition and electricity) by 15%. 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.
NSF Program Director: Ruth Shuman
| Status | Closed |
|---|---|
| Effective start/end date | 02/01/24 → 07/31/26 |
Funding
- I-Corps Teams: $50,000.00
Active Fiscal Year
- FY2024
- FY2026
- FY2025
Start Fiscal Year
- FY2024
TIP Programs
- I-Corps Teams
Key Technology Areas
- Artificial Intelligence
- (confidence score: 100%)
Technology Foci
- Artificial Intelligence (Broad)
- (confidence score: 100%)
Congressional District at Award
- District n. 04 of Tennessee
Current Congressional District
- District n. 04 of Tennessee
United States
- Tennessee
Core Based Statistical Area (CBSA)
- Nashville-Davidson--Murfreesboro--Franklin, TN
County
- County: Rutherford, TN
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