How AI is Transforming African Agriculture: A Conversation with Sandra Eneremadu

By Ugo Aliogo

While much of the conversation around artificial intelligence in Africa centres on fintech and healthcare, a quieter revolution is unfolding in the continent’s farmlands. AI-powered tools are helping farmers combat drought, optimise yields, and participate in global carbon markets, all while working within the constraints of limited connectivity and resources.

Sandra Eneremadu has spent years at this intersection of technology and agriculture. As a Product Manager specialising in sustainable AgriTech, she builds AI-enabled platforms and digital monitoring systems that translate drone imagery, IoT sensors, and satellite data into insights farmers can actually use. We sat down with her to discuss why AI matters for Africa’s agricultural future, what’s working on the ground, and where the gaps still lie.

Why AI Matters for African Agriculture

AI gets plenty of attention in fintech and health, but agriculture seems less visible in those conversations. Why should we be paying more attention?

Sandra Eneremadu: Agriculture still feeds most of Africa, yet it remains one of the least digitised sectors on the continent. That’s a massive gap because farming here operates in an environment of constant uncertainty—unpredictable weather, pest outbreaks, fluctuating input costs, and unreliable supply chains.

AI changes the equation by bringing predictability to that uncertainty. It helps farmers move from guesswork to informed decisions, whether that’s knowing the optimal planting window, calculating precise irrigation needs, or identifying pest threats before they spread.

The real transformation isn’t just about deploying high-tech tools. It’s about democratising intelligence. When a smallholder farmer gets actionable insights delivered in their local language on a basic phone, that’s when productivity and sustainability start moving together.

AI Use Cases Already Delivering Results

What are the most promising applications you’re seeing right now?

Sandra Eneremadu: Digital MRV systems—monitoring, reporting, and verification platforms—are among the most impactful. These systems aggregate data from drones, ground sensors, and satellites, then use AI models to detect patterns invisible to the human eye: early signs of crop stress, soil degradation, or yield variability across different plots.

Take smart irrigation as another example. Machine learning algorithms analyse weather forecasts, soil moisture levels, and crop water requirements to optimise irrigation schedules. In drought-prone regions, that kind of precision can mean the difference between a failed harvest and a successful one.

Beyond individual farms, we’re seeing AI help cooperatives forecast harvests more accurately, allowing them to negotiate better prices and reduce post-harvest losses. Governments and development organisations are using these same tools to build early-warning systems for food insecurity, giving them lead time to intervene before crises escalate.

The Climate Dimension

You emphasise sustainability throughout your work. How does AI contribute to climate action in agriculture?

Sandra Eneremadu: AI gives us something we’ve never had before: real-time visibility into the environmental impact of farming decisions. We can now track soil carbon sequestration, monitor fertiliser runoff, measure methane emissions from livestock, and detect deforestation risks near agricultural zones.

That visibility matters because it creates accountability and opportunity. Farmers who can prove they’re sequestering carbon or reducing emissions can access carbon credit markets and regenerative agriculture programmes that financially reward sustainable practices. What was once just good environmental stewardship becomes an economic incentive.

This is where innovation and climate responsibility converge. We’re not asking farmers to choose between profitability and sustainability anymore; we’re building systems where those goals reinforce each other.

Barriers to Scaling

What’s preventing these solutions from reaching more farmers?

Sandra Eneremadu: Connectivity remains the biggest barrier. Many smallholder farmers operate in areas with limited or no network coverage, and most can’t afford smartphones or data plans. If your AI solution requires constant internet access, you’ve already excluded the majority of the people who need it most.

That’s why human-centred design is critical. We need to build systems that work offline, use SMS or USSD when the internet isn’t available, support local languages, and don’t assume technical literacy. The interface a farmer interacts with matters as much as the algorithm running behind it.

There’s also a trust gap. Farmers have seen plenty of well-intentioned projects fail, so scepticism is rational. Earning trust means proving value quickly, integrating with existing practices rather than demanding wholesale changes, and ensuring farmers maintain agency over their decisions.

If we’re not intentional about inclusivity, we risk creating a two-tier agricultural system where tech-enabled farms thrive while everyone else falls further behind.

Lessons from the Field

What has building AgriTech solutions taught you?

Sandra Eneremadu: Technology alone never creates change; people do. You can build the most sophisticated AI model in the world, but its impact depends entirely on whether a farmer trusts it enough to act on it.

I’ve learned to measure success differently. It’s not about the elegance of the algorithm or the volume of data processed. It’s about whether a farmer changed their planting strategy because of an insight your platform delivered, and whether that decision improved their yield or reduced their costs.

When you witness that moment of connection—when someone realises the technology is genuinely working for them rather than imposing on them—that’s when innovation feels real. It’s humbling and energising in equal measure.

Looking Ahead

What’s your vision for the future of agriculture in Africa?

Sandra Eneremadu: AI is the new fertiliser. It doesn’t replace farmers; it amplifies their capabilities and helps them navigate complexity.

If we continue building inclusively, designing for the farmers who need these tools most rather than those easiest to reach, Africa won’t just adapt to global agricultural trends—we’ll define the next generation of them. The innovations emerging from African farms, built for African contexts, will be the blueprint for resilient, sustainable agriculture everywhere.

Sandra Eneremadu is a Product Manager specialising in sustainable AgriTech solutions. She designs AI-enabled platforms and digital MRV (Monitoring, Reporting, and Verification) systems that help farmers in low-connectivity regions transform data from drones, satellites, and IoT sensors into actionable insights for productivity and climate resilience.