Abstract & Details
Description
Award ID: 2333948
This Small Business Innovation Research (SBIR) Phase I project improves the quality, value, benefits, and life span of nursery stock trees, so they will grow and thrive in the landscape where people live. Trees, with their myriad benefits for human health, ecosystem services, and climate mitigation achieve their full potential when they thrive long term. Current methods fail to adequately address tree root quality. By modernizing root inspection, this technology will not only improve industry standards and boost economic competitiveness, but also promote environmental stewardship on a global scale. With this technology, arborists and growers may be able to identify above-ground tree root defects and take corrective action to promote good quality root systems that are needed for these important tree assets to grow to maturity. This SBIR Phase I project focuses on the development of a 3-dimensional, non-destructive, ground penetrating radar (GPR) computed tomography (CT) system with cutting-edge software analytics to inspect and assess the quality of container-grown root systems in nursery stock trees. This innovation is based on the understanding that the GPR signals are generated by the large differentials between live tissues and the surrounding soil. The technology detects serious root system defects that could cause early tree mortality if not corrected before the tree is planted. The data will be collected with the help of an innovative apparatus designed to seamlessly capture 3D root data from container-grown trees using a commercial GPR system with a wireless antenna that works as a secondary layer around the container, emulating the precision of an X-ray CT scanner. A novel root quality classification model will inform the development of a root analysis software program to build an initial GPR dataset for the machine learning model and subsequently to adopt an active learning approach. Initial experiments will focus on a sufficiently large number of one or two species of trees. 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: Rajesh Mehta
This Small Business Innovation Research (SBIR) Phase I project improves the quality, value, benefits, and life span of nursery stock trees, so they will grow and thrive in the landscape where people live. Trees, with their myriad benefits for human health, ecosystem services, and climate mitigation achieve their full potential when they thrive long term. Current methods fail to adequately address tree root quality. By modernizing root inspection, this technology will not only improve industry standards and boost economic competitiveness, but also promote environmental stewardship on a global scale. With this technology, arborists and growers may be able to identify above-ground tree root defects and take corrective action to promote good quality root systems that are needed for these important tree assets to grow to maturity. This SBIR Phase I project focuses on the development of a 3-dimensional, non-destructive, ground penetrating radar (GPR) computed tomography (CT) system with cutting-edge software analytics to inspect and assess the quality of container-grown root systems in nursery stock trees. This innovation is based on the understanding that the GPR signals are generated by the large differentials between live tissues and the surrounding soil. The technology detects serious root system defects that could cause early tree mortality if not corrected before the tree is planted. The data will be collected with the help of an innovative apparatus designed to seamlessly capture 3D root data from container-grown trees using a commercial GPR system with a wireless antenna that works as a secondary layer around the container, emulating the precision of an X-ray CT scanner. A novel root quality classification model will inform the development of a root analysis software program to build an initial GPR dataset for the machine learning model and subsequently to adopt an active learning approach. Initial experiments will focus on a sufficiently large number of one or two species of trees. 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: Rajesh Mehta
| Status | Closed |
|---|---|
| Effective start/end date | 01/15/24 → 01/31/25 |
Funding
- SBIR Phase I: $274,990.00
Active Fiscal Year
- FY2024
- FY2025
Start Fiscal Year
- FY2024
TIP Programs
- SBIR Phase I
Small Business
- Yes
Key Technology Areas
- Artificial Intelligence
- (confidence score: 89%)
Technology Foci
- Machine Learning Training Data
- (confidence score: 86%)
Congressional District at Award
- District n. 00 of Vermont
Current Congressional District
- District n. 00 of Vermont
United States
- Vermont
Core Based Statistical Area (CBSA)
- Burlington-South Burlington, VT
County
- County: Chittenden, VT
EPSCoR Jurisdiction
- Yes
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