An Advanced Information System for Timely Assessments of International Crop Market Opportunities

Award Information
Agency: Department of Agriculture
Branch: N/A
Contract: N/A
Agency Tracking Number: 2007-00197
Amount: $349,324.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 2008
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
200 INNOVATION BLVD STE 234, State College, PA, 16803
DUNS: 122713014
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dmitry Varlyguin
 (814) 237-4060
Business Contact
 Stephanie Hulina
Title: President & Senior Scientist
Phone: (814) 237-4060
Research Institution
Situation or Problem In order to provide timely and reliable assessments of the global crop market, it is necessary to move from limited, in situ crop assessments to operational crop monitoring on regional and global scales with multi-temporal remotely sensed (RS) data. Due to timeliness requirements and large volumes of imagery needed for operational agricultural monitoring, it is important that data processing and management, crop models, decision support systems, and output products are automated to the fullest extent possible. Lack of automation at the analytical step is one of the main issues precluding the introduction of RS time-series data into operational, near-real time settings. Purpose Development of an advanced, innovative system in support of timely assessments of emerging market opportunities for U.S. commodity crops is proposed. Remotely Sensed time-series data will be used to monitor crop conditions, compare given year conditions with those of the previous years, and to forecast relative crop yield on a bi-monthly basis. The overall results of the project will enhance regional and global agricultural monitoring and improve the timeliness and accuracy of current and projected crop commodity information relative to temporal marketing opportunities. The algorithms will improve FAS operational processing of time-series data and will contribute to FAS marketing and trade assessments.

* Information listed above is at the time of submission. *

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