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SBIR Phase I: High-throughput 3-D Insect Data Capture Enabling Large Scale…

Award Information

Agency:
National Science Foundation
Branch:
N/A
Award ID:
Program Year/Program:
2012 / SBIR
Agency Tracking Number:
1143119
Solicitation Year:
2012
Solicitation Topic Code:
BC
Solicitation Number:
Small Business Information
SR2 GROUP, LLC
14900 Sweitzer Lane Suite 101 Laurel, MD 20707-2915
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2012
Title: SBIR Phase I: High-throughput 3-D Insect Data Capture Enabling Large Scale Specimen Digitization and Machine Species Identification
Agency: NSF
Contract: 1143119
Award Amount: $149,999.00
 

Abstract:

This Small Business Innovation Research (SBIR) Phase I is directed toward high-throughput robotic imaging and data capture of 3D physical insect specimens with applications to national intelligence and security, vector epidemiology, agriculture, biodiversity research, and museum curation. Natural history museums are vast, largely untapped sources of biological data for addressing complex questions ranging from tracking terrorists to climate change. Yet ~1 billion U.S. specimens remain in museum drawers and cabinets, their characteristics, associated data, and even their existence hidden. Insects preserved on pins represent a significant proportion, with the ten largest collections housing over 100 million. While the scope of collections is immense, the expertise needed to identify insects of importance is in decline as taxonomists retire and university positions close. For some insect groups, few or no experts remain capable of identifying species. The proposed technology will radically accelerate the pace of insect specimen digitization. It will capture critical data from museum specimens and revitalize taxonomy by providing datasets for computer-automated insect identification efforts. The project demonstrates robotic handling of pinned specimens is feasible, enabling a high-throughput system capable of manipulating pinned specimens, partitioning the 3D scene, photographing specimens, and extracting database information with OCR rendering of specimen labels. The broader impact/commercial potential of this project spans myriad applications. Insects vector important diseases of humans, livestock, and crops. Annually, malaria is implicated in a million human deaths worldwide and aphids and their viruses cost US farmers over one billion dollars. New invasive insects regularly arrive on our shores undetected. Insects also have forensics applications, locating events in time and place and calculating times of death. Tools to quickly and efficiently identify insects have immediate application in vector epidemiology, agricultural and environmental monitoring, border security and port inspection, law enforcement, and intelligence. Spatial-temporal data associated with field collection of insect specimens are crucial for geolocation forensics, tracking climate change and range expansion of invasive species, and locating geographic sources of potential biological control agents for exotic pests. Current technologies and specimen digitization practices take too much time, cost too much money, and simply cannot image most specimens. Semi-automated systems address only microscope slide- or paper-mounted specimens. Currently no technology exists to even partially automate the digitization of individual pinned insect specimens. Potential customers include insect collections, museums, agribusinesses, epidemiologists, and agents in homeland security, port inspection, environmental monitoring, and national intelligence. Enormous secondary commercial potential exists with value-added online databases.

Principal Investigator:

Jeffrey M. Sieracki
3016040001
NSFContact@sr2group.com

Business Contact:

Jeffrey M. Sieracki
3016040001
NSFContact@sr2group.com
Small Business Information at Submission:

SR2 Group, LLC
14900 Sweitzer Lane Suite 101 Laurel, MD 20707-2915

EIN/Tax ID: 200497927
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No