United StatesFood and restaurants2017 – ongoingE. & J. Gallo Winery
Machine-learning precision vineyard irrigation
Gallo reported 20% less water per pound of grapes with precision irrigation
Problem
Uniform irrigation left some vines overwatered and others short of water. Water requirements varied within vineyard blocks.
Approach
Gallo and IBM Research developed irrigation controls using satellite imagery and soil and weather data. Physics-inspired machine learning established watering schedules, and connected valves delivered water locally. Deployment covered 6 California vineyard blocks.
Results
Takeaway
The reported saving measured water per pound of grapes, rather than total vineyard water consumption.
Categories
- Industry
- Food and restaurants
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and MLOptimization
- Adoption
- Partner co-development
- Evidence
- Company's own claim
Sources
Source summaries
Gallo and IBM developed machine-learning irrigation schedules and local watering controls. Gallo reported 20% less water per pound of grapes across equipped blocks covering nearly 250 acres.
Full details are in the original sources.