Matching Agricultural Needs to Research and Technology
How might AI help ensure Washington farmers’ real problems inform research and technology development?
Between 2015 and 2023, the number of university extension faculty, who bring research and technical advice directly to farmers, fell by nearly 36%. At the same time, producers consistently report a shortage of locally relevant research and technical assistance to guide their decisions. Further, the computer science and engineering specialists developing new tools often do not understand agriculture. They are often drawn to other sectors or develop tools without an understanding of the real problems, which inevitably fail.
The opportunity is not only more research but a better innovation ecosystem for agriculture. Producers would benefit most from technology developed in concert with the people who will use it, rather than built in isolation from the farm. Agriculture practitioners need opportunities to opt into research and technology development programs that are personally valuable and reward them for testing new technologies and sharing data.
AI stands to make research and technology more impactful by keeping researchers and technologists grounded in what farmers actually need, from how their problems are captured to how solutions are tested and delivered back to the farm.
Opportunity Areas
The opportunity areas below are meant to inspire, not prescribe. The best ideas may come from directions we haven’t anticipated. We welcome ideas that address one area or span several.
How might AI let researchers and developers test ideas with real farmers and improve them quickly, before committing to large-scale adoption?
Farmers on thin margins cannot afford to be early adopters of unproven technology, and developers cannot get the grounded field feedback they need, so promising tools stall before they reach the farm.
Ideas might include:
- Shared testbeds or innovation hubs that pair developers with producer test sites, providing sensing infrastructure, data, and agronomic expertise to de-risk early trials
- Systems that aggregate and protect on farm data across many farms, letting developers iterate quickly and farmers see what works across similar operations
- Shared-cost pilots or outcome-based incentives that lower the barrier for farmers to participate in testing
How might AI gather the real, on-the-ground problems farmers face and turn them into clear priorities that researchers and developers can act on?
Producers report a shortage of locally relevant research, a sign that the work being done does not always reach the problems they most need solved.
Ideas might include:
- Platforms that capture unmet needs from growers through voice, text, or field reporting and synthesize them into research and development priorities funders and institutions can act on
- Tools that help extension agents and researchers surface the most common and pressing challenges across a distributed farming community
How might AI close the loop by getting research and technical guidance into producers’ hands, especially as extension capacity shrinks?
As public research and extension capacity declines, fewer professionals are available to translate research into guidance and deliver it to the farms that need it.
Ideas might include:
- Tools that translate complex research into practical, farm-specific guidance for producers who lack the time or background to engage with it directly
- Systems that extend the reach of a smaller number of extension professionals across more farms and regions
- Platforms that help commodity commissions and extension services track what guidance reaches producers and whether it changes outcomes
