2247237
Cooperative Agreement
Overview
Grant Description
SBIR Phase II: Real-time computer automated identification and quantification of insects entering the SolarID Insect Control Device (ICD) - The broader impacts of this Small Business Innovation Research (SBIR) Phase II project include an artificial intelligence technology designed to detect, identify, and determine levels of insect infestations in fields, providing a comprehensive decision support system in real-time.
More efficient and precise insect monitoring would result in reduced chemical insecticide use by increasing the specificity and timeliness of the applied input. Successful completion of the project could serve to increase the economic competitiveness of the U.S. in the world agricultural market, positively impact the health and welfare of the American public through reduced pesticide use, and introduce rural populations to technology highlighting the benefits of investment in science, technology, engineering, and math (STEM) education.
This technology could result in significant savings per acre through decreased expenditures on pesticides and decreased damage done by pests. Considering pests cause $45 billion per year in crop damage annually, and US farms spend more than $25 billion per year on pesticides, the savings to the industry could be substantial.
The project provides an artificial intelligence (AI)-driven insect trapping system that can identify a broad diversity of insects in real-time. The primary objective of the project is to finish development of an integrated pest management tool that attracts, captures, and images pest insects, identifies and counts them in real-time, and delivers data and management decisions in a user-friendly format to internet-accessible devices.
In order to achieve this objective, the technology will be deployed in several agricultural systems where they will continuously obtain data in the form of insect images. The insects in these images will be identified by experts and the data will be used to train the AI insect identification system. During the project, the system will learn to identify a diversity of important and commonly encountered insects in agricultural fields and orchards, and a user-friendly interface for delivering results to users will be developed.
The end goal is an all-in-one pest management tool that can be deployed in any agricultural system in the United States where it can aid farmers in the management of pest problems while minimizing pesticide use and increasing yields. 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. - Subawards are planned for this award.
More efficient and precise insect monitoring would result in reduced chemical insecticide use by increasing the specificity and timeliness of the applied input. Successful completion of the project could serve to increase the economic competitiveness of the U.S. in the world agricultural market, positively impact the health and welfare of the American public through reduced pesticide use, and introduce rural populations to technology highlighting the benefits of investment in science, technology, engineering, and math (STEM) education.
This technology could result in significant savings per acre through decreased expenditures on pesticides and decreased damage done by pests. Considering pests cause $45 billion per year in crop damage annually, and US farms spend more than $25 billion per year on pesticides, the savings to the industry could be substantial.
The project provides an artificial intelligence (AI)-driven insect trapping system that can identify a broad diversity of insects in real-time. The primary objective of the project is to finish development of an integrated pest management tool that attracts, captures, and images pest insects, identifies and counts them in real-time, and delivers data and management decisions in a user-friendly format to internet-accessible devices.
In order to achieve this objective, the technology will be deployed in several agricultural systems where they will continuously obtain data in the form of insect images. The insects in these images will be identified by experts and the data will be used to train the AI insect identification system. During the project, the system will learn to identify a diversity of important and commonly encountered insects in agricultural fields and orchards, and a user-friendly interface for delivering results to users will be developed.
The end goal is an all-in-one pest management tool that can be deployed in any agricultural system in the United States where it can aid farmers in the management of pest problems while minimizing pesticide use and increasing yields. 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. - Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH PHASE II (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE II", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22552
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Clinton,
Arkansas
72031-6012
United States
Geographic Scope
Single Zip Code
Related Opportunity
22-552
Solarid Ar was awarded
Cooperative Agreement 2247237
worth $981,168
from National Science Foundation in October 2023 with work to be completed primarily in Clinton Arkansas United States.
The grant
has a duration of 2 years and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase II
Title
SBIR Phase II:Real-time computer automated identification and quantification of insects entering the SolaRid insect control device (ICD)
Abstract
The broader impacts of this Small Business Innovation Research (SBIR) Phase II project include an artificial intelligence technology designed to detect, identify, and determine levels of insect infestations in fields, providing a comprehensive decision support system in real-time.More efficient and precise insect monitoring would result in reduced chemical insecticide use by increasing the specificity and timeliness of the applied input. Successful completion of the project could serve to increase the economic competitiveness of the U.S. in the world agricultural market, positively impact the health and welfare of the American public through reduced pesticide use, and introduce rural populations to technology highlighting the benefits of investment in science, technology, engineering and math (STEM) education. This technology could result in significant savings per acre through decreased expenditures on pesticides and decreased damage done by pests. Considering pests cause $45 billion per year in crop damage annually, and US farms spend more than $25 billion per year on pesticides, the savings to the industry could be substantial._x000D_ _x000D_ The project provides an artificial intelligence (AI)-driven insect trapping system that can identify a broad diversity of insects in real-time. The primary objective of the project is to finish development of an integrated pest management tool that attracts, captures, and images pest insects, identifies and counts them in real-time, and delivers data and management decisions in a user-friendly format to internet-accessible devices. In order to achieve this objective, the technology will be deployed in several agricultural systems where they will continuously obtain data in the form of insect images. The insects in these images will be identified by experts and the data will be used to train the AI insect identification system. During the project, the system will learn to identify a diversity of important and commonly encountered insects in agricultural fields and orchards and a user-friendly interface for delivering results to users will be developed. The end goal is an all-in-one pest management tool that can be deployed in any agricultural system in the United States where it can aid farmers in the management of pest problems while minimizing pesticide use and increasing yields._x000D_ _x000D_ 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.
Topic Code
BT
Solicitation Number
NSF 22-552
Status
(Complete)
Last Modified 10/6/23
Period of Performance
10/1/23
Start Date
9/30/25
End Date
Funding Split
$981.2K
Federal Obligation
$0.0
Non-Federal Obligation
$981.2K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2247237
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
YA9SUGXDSN51
Awardee CAGE
8BLW8
Performance District
AR-02
Senators
John Boozman
Tom Cotton
Tom Cotton
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $981,168 | 100% |
Modified: 10/6/23