SBIR/STTR Research Award

Low Cost Sensing and Assessment of Grapevine Canopy Density for Improved Grape Production and Quality

Award identifier f87cc00e017ee49c4533d913a5c47da9c89a79c84e487cee1ca56772470ff8ccAwarding agency Department of Agriculture

Award amount

$99,915

Program and phase

SBIR · Phase I

Award year

2015

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Agriculture
Branch
Not reported
Recipient company
MOAI TECHNOLOGIES, LLC
Proposal award date
2015-06-03
Contract end date
Not reported
UEI
HKRCLDA9BG75
Solicitation number
Not reported
Topic code
8.13

Research abstract

This project will develop and test a solution that will provide grapevine canopy density measures using low-cost off-the-shelf LIDAR (Light Detection and Ranging) sensors and intelligent processing. It will be affordable, usable by vineyard workers with minimal training, and have high diagnostic value to the vineyard manager who is responsible for making canopy management decisions and for the quality of the crop. As wine exports continue to increase from low cost countries such as Chile and Argentina, the U.S. wine industry must find ways to stay competitive. This will be even more important in the future as the prospect of a rapidly growing Chinese wine industry producing and exporting high quality wines becomes a reality. One way the wine industry in the U.S. can maintain its place in the market is to leverage technology and automation that both reduces the amount of labor required for grape production and increases the quality of the grape crop leading to higher quality wines. Grapevine canopy management is perhaps the most critical task in the production of uniformly high quality grapes and is directly related to wine quality. Also, good canopy management practices result in less disease, reducing the need for fungicide sprays. Thinning the canopy by means of hedging, topping, and leaf removal can be done by machinery. However, in current practice, the task of evaluating the vine canopy so that thinning can be done at exactly the right time and to the right extent, is manual and labor-intensive. The proposed system will greatly reduce the amount of labor required to assess the canopy density in a large commercial vineyard by largely automating the task with tractor-mounted sensors and automated data analysis. It will allow an entire vineyard canopy to be mapped with just one pass of a tractor and pinpoint especially problematic areas that should be the focus of management tactics. It will also help to reduce the amount of pesticides used in U.S. vineyards, by reducing conditions in the vineyard that foster grape fungus development.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

20 SBIR/STTR records share this UEI (2014–2023). Reported award amounts total $13,926,178. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Health and Human Services, National Science Foundation, Department of Agriculture

Agency intelligence

Department of Agriculture

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.