Page 115 - AI Standards for Global Impact: From Governance to Action

AI Standards for Global Impact: From Governance to Action

17 AI for agriculture – shaping standards for smart food systems

AI has the potential to help transform agriculture. By fostering trust and sound governance,
AI-driven food systems can become smarter, fairer, and more resilient for all stakeholders. The Part 2: Thematic AI
AI for agriculture – Shaping standards for smart food systems workshop provided an overview
of how AI will change the digital agriculture landscape. AI encompasses a range of digital tools
and platforms designed to be accessible, interoperable, and secure, fostering connectivity,
communication, and innovation.

All Presentations are available here.

17�1 Dialogue on digital agriculture roadmap

The dialogue discussed priorities for a collaborative Digital Agriculture Roadmap that harnesses
AI and emerging technologies to build resilient, inclusive, and sustainable food systems could.
Discussions explored key enablers such as data governance, interoperability, and standards,
and partnerships between governments, private sector, and local communities. They also
considered scalable AI based solutions to enhance productivity, reduce food waste, and
empower smallholder farmers, with the aim of ensuring that digital transformation in agriculture
is both equitable and future-ready. This workshop was a follow up to FAO’s First Global Dialogue
on AI in Agriculture held from 28 to30 April 2025.

The development of a "Digital Agriculture & AI Innovation Roadmap" led by FAO focuses on
accelerating the adoption of digital agriculture and AI to build efficient, inclusive, resilient,
and sustainable agri-food systems. It aims to address global challenges such as climate
shocks, geopolitical instability, resource depletion, and the demands of a fast-growing global
population. Targeting governments, NGOs, academia, private firms, and investors, especially
in low- and middle-income countries, the roadmap highlights advancements in AI applications,
implementation challenges, and strategies for diverse agricultural settings.

Many companies in the agriculture sector still store their data in silos instead of in an integrated
platform accessible to different areas of their business. For example, climate-related data can be
held by a department without immediate access to data on issues such as yields and financial
outcomes. Data analytics and modelling requires large amounts of data. Companies that help
create and engage in data-sharing ecosystems can accumulate more data faster, benefiting
the whole value chain. Integrating technologies such as remote sensing (satellites and drones),
water quality sensors, and crop monitors with AI and advanced data analytics enables actionable
insights for farmers. Strong data governance, meanwhile, is key to maximizing data quality and
security.

An example of what is possible when data is unified would be a digital farming platform aimed at
giving farmers access to a wide range of data to support regenerative efforts. The platform can
obtain data from satellites, sensors, farm equipment, and other sources, and make it available
via AI-driven visualization and analysis. Farmers would get insights like which biodiversity actions
are best suited for their farm.

As different players invest in data collection, sharing, and governance, their efforts will benefit
their partners, creating a symbiotic relationship that increases the resilience of the industry as
a whole.

103