SBIR Phase I: Online High Fidelity 3D Modeling of Produce Using Low Cost Sensors

Award Information
National Science Foundation
Award Year:
Phase I
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
Neya Systems, LLC
12330 Perry Hwy, Suite 220, Wexford, PA, -
Hubzone Owned:
Socially and Economically Disadvantaged:
Woman Owned:
Principal Investigator:
Parag Batavia
(724) 799-8078
Business Contact:
Parag Batavia
(724) 799-8078
Research Institution:

This Small Business Innovation Research (SBIR) Phase I project investigates the feasibility of employing high?resolution, colorized, 3D models of food produce captured with structured light technology to identify (ID) and sort produce by quality. The feasibility of accomplishing this within the real-time constraints of in-field harvesting will be determined. The PROBLEM being addressed is that high fidelity modeling currently requires controlled calibration and acquisition processes, expensive sensor hardware and time consuming global optimization algorithms taking minutes, or hours, to complete. The proposed APPROACH is to research novel processing algorithms to extend 2D super-resolution principles and exploit new probabilistic motion and sensor error models to achieve precise multi-frame registration and fast global optimization. This research will exploit new methods which operate on smaller clusters of ?similar? pixels and leverage 3D probabilistic occupancy mappings, taking advantage of imaging and geometry features simultaneously to reduce computation time and noise. If achievable, the BENEFITS include development of a cost-effective and feature-rich advanced data modeling technology that can be integrated into produce collection machinery and used to cost-effectively segment individual food items based on cosmetic imperfections. This is expected to provide both reduced costs and increased revenues to small and medium farm enterprises. The broader impact/commercial potential of this project addresses two areas: 1) a critical gap within domestic produce farming which prevents small farmers from competing with large, corporate enterprise farms in terms of efficiency, quality control and product pricing and 2) a very real advance in the broader area of sensor systems and technology. Although a distributed, low cost modeling and sorting application for fruits and vegetables is targeted in Phase I, the technology should provide similar benefits to the broader consumer foods market by improving the distribution and sorting of meats, seafood, cheeses or even baked goods, and the like, while also advancing many other areas that can benefit from good ID and sorting technology. At the very least, this cost effective, automated, sorting capability is expected to increase small farm revenues by at least 25% and provide the offeror with a burgeoning, worldwide business through its strategic partners. In addition, such an affordable, and fast, 3D data modeling and inspection technology for accurately categorizing geometric defects can be expected to have a much broader impact within the field of data modeling and inspection.

* information listed above is at the time of submission.

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