2423424
Project Grant
Overview
Grant Description
Sttr Phase I: Commercial applications of CropMap (monitoring, analysis, and prediction) for oil seed fields - the broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project involves the development and evaluation of the Crop Ecosystem Monitoring, Analysis, and Prediction (CropMap) tool.
This project addresses the critical need to support food security profitability by optimizing resource management and decision-making through advanced monitoring and predictive analytics in crop production.
The significance of this research lies in its potential to enhance agricultural productivity and sustainability across the United States, thereby improving the lives of farmers by increasing yield outputs and reducing losses.
Furthermore, the successful commercialization of CropMap could generate substantial economic benefits, including increased tax revenues and job creation in the agricultural sector.
By aligning with NSF’s mission to advance the progress of science, this project contributes to the scientific understanding of agricultural ecosystems and impacts related fields such as environmental science and economics.
This project represents a significant technical innovation in the field of precision agriculture through the development of the CropMap tool, a high-risk effort with substantial potential for high impact.
CropMap integrates novel algorithms and models with real-time data feeds for enhanced monitoring and predictive analytics of crop conditions.
The primary innovation involves the application of machine learning techniques to satellite images and climate data to predict crop yields, water usage, and soil health more accurately than current methods allow and the use of artificial intelligence to make actionable insights timely available to technical and non-technical users.
The goals of this project are to validate these models' effectiveness in real-world settings and to establish a scalable framework for its application across various agricultural contexts.
The project will employ rigorous methodological approaches, including the use of time-series image analytics and data-driven diagnostic models, to achieve these objectives.
Through its focus on innovation and scalability, the project aims to set a new standard in agricultural practices, ultimately facilitating better resource management and sustainability.
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.
This project addresses the critical need to support food security profitability by optimizing resource management and decision-making through advanced monitoring and predictive analytics in crop production.
The significance of this research lies in its potential to enhance agricultural productivity and sustainability across the United States, thereby improving the lives of farmers by increasing yield outputs and reducing losses.
Furthermore, the successful commercialization of CropMap could generate substantial economic benefits, including increased tax revenues and job creation in the agricultural sector.
By aligning with NSF’s mission to advance the progress of science, this project contributes to the scientific understanding of agricultural ecosystems and impacts related fields such as environmental science and economics.
This project represents a significant technical innovation in the field of precision agriculture through the development of the CropMap tool, a high-risk effort with substantial potential for high impact.
CropMap integrates novel algorithms and models with real-time data feeds for enhanced monitoring and predictive analytics of crop conditions.
The primary innovation involves the application of machine learning techniques to satellite images and climate data to predict crop yields, water usage, and soil health more accurately than current methods allow and the use of artificial intelligence to make actionable insights timely available to technical and non-technical users.
The goals of this project are to validate these models' effectiveness in real-world settings and to establish a scalable framework for its application across various agricultural contexts.
The project will employ rigorous methodological approaches, including the use of time-series image analytics and data-driven diagnostic models, to achieve these objectives.
Through its focus on innovation and scalability, the project aims to set a new standard in agricultural practices, ultimately facilitating better resource management and sustainability.
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.
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding Agency
Place of Performance
Norman,
Oklahoma
73072-7337
United States
Geographic Scope
Single Zip Code
American Prime Sustainable Solutions was awarded
Project Grant 2423424
worth $275,000
from in October 2024 with work to be completed primarily in Norman Oklahoma United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
STTR Phase I
Title
STTR Phase I: Commercial applications of CropMAP (Monitoring, Analysis, and Prediction) for oil seed fields
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project involves the development and evaluation of the Crop Ecosystem Monitoring, Analysis, and Prediction (CropMAP) tool. This project addresses the critical need to support food security profitability by optimizing resource management and decision-making through advanced monitoring and predictive analytics in crop production. The significance of this research lies in its potential to enhance agricultural productivity and sustainability across the United States, thereby improving the lives of farmers by increasing yield outputs and reducing losses. Furthermore, the successful commercialization of CropMAP could generate substantial economic benefits, including increased tax revenues and job creation in the agricultural sector. By aligning with NSF’s mission to advance the progress of science, this project contributes to the scientific understanding of agricultural ecosystems and impacts related fields such as environmental science and economics.
This project represents a significant technical innovation in the field of precision agriculture through the development of the CropMAP tool, a high-risk effort with substantial potential for high impact. CropMAP integrates novel algorithms and models with real-time data feeds for enhanced monitoring and predictive analytics of crop conditions. The primary innovation involves the application of machine learning techniques to satellite images and climate data to predict crop yields, water usage, and soil health more accurately than current methods allow and the use of artificial intelligence to make actionable insights timely available to technical and non-technical users. The goals of this project are to validate these models' effectiveness in real-world settings and to establish a scalable framework for its application across various agricultural contexts. The project will employ rigorous methodological approaches, including the use of time-series image analytics and data-driven diagnostic models, to achieve these objectives. Through its focus on innovation and scalability, the project aims to set a new standard in agricultural practices, ultimately facilitating better resource management and sustainability.
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
R
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 1/14/25
Period of Performance
10/1/24
Start Date
9/30/25
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2423424
Additional Detail
Award ID FAIN
2423424
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
XUH6WGX68JW5
Awardee CAGE
9DVL3
Performance District
OK-04
Senators
James Lankford
Markwayne Mullin
Markwayne Mullin
Modified: 1/14/25