Artificial intelligence (AI) is emerging as a potentially powerful tool for transforming agriculture across Africa, helping farmers respond to climate change, improve productivity, reduce costs and make better decisions.

For a continent where nearly 60% of the workforce in sub-Saharan Africa depends on agriculture, the stakes are significant. Yet millions of farmers continue to operate small plots while facing unpredictable rainfall, rising climate risks, pests, limited access to agricultural advice and difficulty reaching profitable markets.

The challenge is not simply producing more food. It is helping farmers become more productive and resilient in an increasingly uncertain environment.

That is where AI could play a growing role.

From Precision Farming to Better Predictions

Dr Samuel Babatunde, Director of Operations at Nigerian agricultural production company SBZ Development, believes AI has the potential to address some of the sector’s most persistent challenges.

Applications range from precision farming and weather forecasting to soil analysis, pest detection and digital market intelligence.

“Evidence indicates that AI is able to increase crop production by about 15%–30%, lower input costs by 10%–25% with more efficient use of resources, and increase net farm income by 20%–40% when combined with weather prediction, soil analytics, and digital market intelligence,” Babatunde tells FORBES AFRICA.

The significance of these technologies is not limited to higher yields.

AI could help farmers determine when to plant, how much fertilizer to apply, where crops require additional water and when weather conditions could threaten production.

Instead of relying entirely on historical experience or generalized agricultural advice, farmers could increasingly receive recommendations based on real-time and location-specific information.

AI and the Smallholder Farmer

Africa’s agricultural opportunity is closely tied to its smallholder farmers.

Many operate on limited acreage and have few resources to absorb the impact of a failed harvest. A pest outbreak, drought or unexpected change in rainfall can therefore have consequences that extend beyond one farm and affect household incomes and local food supplies.

AI could help reduce some of that uncertainty.

Machine-learning systems can analyze information from weather forecasts, satellite imagery, soil data and crop images to identify patterns that may not be immediately visible to farmers.

For example, an AI-enabled system could potentially identify early signs of crop disease from an image captured on a smartphone. Other systems could use weather and soil information to help determine irrigation requirements or predict periods of increased pest activity.

The objective is not to make farming entirely automated.

Rather, AI can provide farmers and agricultural professionals with information that allows them to make better decisions.

“As a professional at the nexus of environmental sustainability, data-driven decision-making, and agricultural systems, I believe Africa’s greatest opportunity is not just to adopt AI but to create affordable, locally relevant AI solutions designed for the realities of smallholder farmers,” Babatunde says.

Climate Change Makes AI More Important

Climate change is adding another layer of uncertainty to African agriculture.

Changing rainfall patterns, extreme temperatures, droughts and floods can make traditional approaches to farming increasingly difficult to predict.

AI-powered weather and climate tools could help farmers prepare for these changes.

More accurate forecasts could allow farmers to adjust planting dates, select appropriate crops and plan irrigation. Early-warning systems could also provide information about potential droughts, floods or pest outbreaks.

When combined with climate-smart agricultural practices, AI could therefore become a tool for building resilience rather than simply increasing production.

Babatunde argues that AI’s greatest contribution may ultimately be its ability to reduce uncertainty.

“Besides increasing production, AI’s biggest contribution is reducing uncertainty by predicting pest outbreaks, spotting nutrient deficiencies early, forecasting climate risks, and linking farmers to better markets to bolster resilience and food security,” he explains.

Connecting Farmers to Better Markets

The agricultural challenge does not end when a crop is harvested.

Farmers can lose significant income because of limited market information, poor transportation networks, intermediaries and an inability to identify buyers offering better prices.

AI could help address part of this problem through digital market intelligence.

By analyzing prices, demand, supply and other market information, digital platforms could help farmers make more informed decisions about what to produce, when to sell and where demand is strongest.

This could shift agricultural technology away from focusing exclusively on production and toward the entire value chain.

Better information about markets could also reduce waste by helping farmers and buyers coordinate supply more effectively.

Lessons From AI in Healthcare

Agriculture is not the only sector where African innovators are adapting AI to local conditions.

In South Africa, health technology company Nexus Intelligence has developed an AI system capable of analyzing chest X-rays for tuberculosis and other lung abnormalities, including in environments where reliable internet connectivity is unavailable.

The system has been deployed at sites including the Mponeng Gold Mine, nearly four kilometers underground.

The example highlights an important lesson for agricultural AI: technologies designed for Africa cannot always assume reliable broadband, sophisticated infrastructure or abundant technical resources.

An agricultural AI platform intended for smallholder farmers may need to work on basic smartphones, function with limited connectivity and deliver information in locally appropriate formats and languages.

In other words, the most effective AI solutions may be those designed around existing African realities rather than technologies that require those realities to change first.

The Infrastructure Challenge

For AI to have a meaningful impact on African agriculture, however, technology must be accompanied by investment in digital infrastructure.

Farmers need access to affordable connectivity, smartphones and other digital tools. They also need reliable agricultural data and systems capable of turning that data into practical recommendations.

There is another challenge: trust.

Farmers need to understand why an AI system is making a particular recommendation and have confidence that the information is relevant to their circumstances.

Data governance will also become increasingly important as agricultural platforms collect information about farms, production, weather patterns and farmers themselves.

The technology must therefore be affordable, transparent and designed around the needs of the people expected to use it.

Africa’s Opportunity to Build, Not Just Adopt, AI

The opportunity for African agriculture may extend beyond using AI developed elsewhere.

African researchers, entrepreneurs and agricultural professionals can build systems based on the continent’s own farming conditions, crops, climates and languages.

That could be particularly important because agricultural conditions vary dramatically across Africa. A recommendation that works for a commercial farm in one region may not be appropriate for a smallholder farmer elsewhere.

Locally developed AI could incorporate regional knowledge and data to produce more relevant recommendations.

This approach could also create new opportunities for African technology companies operating at the intersection of agriculture, data science, climate technology and financial services.

Toward a More Resilient Food System

AI will not solve Africa’s agricultural challenges on its own.

Access to finance, roads, irrigation, storage, electricity, extension services and functioning markets will remain critical to the future of farming.

But AI could strengthen many of these systems by helping farmers and agricultural businesses make better decisions.

The most promising opportunity may lie in combining AI with existing agricultural knowledge rather than treating technology as a replacement for farmers or agricultural professionals.

When integrated with climate-smart farming, effective extension services and accessible digital infrastructure, AI could help make smallholder agriculture more productive, profitable and resilient.

For Africa, where agriculture remains central to employment, livelihoods and food security, the rise of AI could represent more than a technological shift.

It could become a new tool for managing one of the continent’s most important challenges: how to produce more food, with fewer resources, in a changing climate.