Maize yields in the American Midwest routinely exceed 10 tons per hectare, while smallholder farmers across sub-Saharan Africa harvest barely one-fifth of that—around 2 to 3 tons per hectare. That gap isn't a mystery of nature; it's a mirror of inequality. Precision agriculture technologies powered by artificial intelligence are transforming farming in wealthy nations, but a sweeping review of global AI adoption reveals a stark risk: without deliberate policy intervention, the technology could deepen the divide between rich and poor farmers rather than close it.
The pressure on agriculture has never been greater. Climate change, land degradation, labor shortages, and the relentless demand to feed a growing global population are straining food systems everywhere. Yet the tools available to farmers vary wildly by geography and wealth. In the United States, China, Europe, and Australia, AI-driven systems now monitor crop health in real time, optimize irrigation, predict yields with precision, and reduce post-harvest waste. These advances are built on reliable electricity, broadband connectivity, satellite data, and institutional support—infrastructure most smallholder farmers simply don't have.
The stakes are enormous because smallholder farmers comprise around 80% of all farmers in developing countries. Many of them rely on rain-fed agriculture, use low-yielding local seed varieties, apply inputs inefficiently, and depend on manual labor. These conventional practices were already straining to meet modern food system demands; AI could accelerate their productivity gains, but only if they can access it.
Recent scholarship spanning applied economics, development, and agricultural systems reveals the paradox. AI has demonstrated strong potential to improve productivity and resilience—conserving soil and water, monitoring livestock, reducing losses. But that potential hinges entirely on three pillars: supportive policies, reliable infrastructure, and equitable access. Without them, the technology becomes another tool that concentrates advantage among those who already have it.
The infrastructure gap is brutal in its specificity. Developed countries benefit from continuous digital systems powered by reliable electricity, cloud platforms, satellite networks, and connected sensors that feed real-time data to farmers. Strong institutional frameworks govern data privacy, transparency, and accountability, enabling responsible innovation. Sub-Saharan Africa, South Asia, and low- and middle-income regions often lack these basics. Digital literacy remains patchy. Extension services that help farmers adopt new techniques are thin on the ground. Broadband connectivity, where it exists, is frequently unreliable or prohibitively expensive.
Beyond infrastructure looms a thicket of ethical concerns. Who owns the data collected from a smallholder's fields? How are algorithms tested for bias—do they perform equally well for millet as for wheat, for West African soil as for European loam? What happens to agricultural workers displaced by automation in economies where farming is often the only livelihood available? These questions remain largely unresolved in developing regions, even as they become urgent.
The opportunity remains genuine. AI adoption could help smallholder farmers adapt to climate variability, stretch scarce water and fertilizer supplies, and boost yields substantially. But realizing that vision demands more than technology transfer. It requires governments and international partners to build digital infrastructure, establish clear governance frameworks, support farmer training, and ensure that the benefits of AI agriculture don't flow only to those who can already afford to innovate alone. The choice now is whether AI becomes a bridge to equitable progress or another widening chasm.