AI for Food Security: What Nigeria and South Africa Need for Sustainable Farming

The study compares two major African economies with different agricultural systems, levels of digital development and barriers to reliable food access. Researchers argue that AI can support lasting food security when investment in technology comes with fair access, effective public institutions and environmental protection.

A farmer deciding when to plant needs dependable weather information, affordable supplies and a realistic chance of selling the harvest at a fair price. Artificial intelligence could support these decisions by spotting crop problems, forecasting demand and helping farmers manage limited water supplies. Its usefulness depends on whether farming communities can access the technology and turn its advice into practical action.

Researchers Lere Amusan and Adetayo Sowale explore these issues in "Prospects and challenges of artificial intelligence for food security sustainability in Africa: case of South Africa and Nigeria," published in Frontiers in Sustainable Food Systems. The study compares two major African economies with different agricultural systems, levels of digital development and barriers to reliable food access. Researchers argue that AI can support lasting food security when investment in technology comes with fair access, effective public institutions and environmental protection.

Food security covers the availability of food, people's ability to obtain it, its nutritional value and safety, and dependable access over time. Sustainability means protecting those conditions without damaging the land, water resources and livelihoods that future generations will need. A larger harvest offers limited reassurance to families who cannot afford nutritious meals or farmers whose soil is becoming less productive.

What the Research Examined

The researchers conducted a comparative review of existing publications and institutional reports, using approximately 15 selected sources for their main analysis. They examined agricultural applications, infrastructure, governance, finance, digital skills and environmental pressures through a qualitative approach. The work involved no new farm trials or interviews, so its findings describe opportunities and constraints rather than directly measured improvements caused by AI.

Two ideas guide the analysis: embedded liberalism, which connects markets with public protections, and agroecology, which treats farming as part of an interconnected ecosystem. In everyday terms, these approaches ask whether technology serves farmers fairly and protects soil, water, biodiversity and local knowledge. The researchers use both perspectives to assess benefits beyond higher production, including farmer independence and communities' control over their food systems.

The evidence has limitations because published accounts can favour successful projects, national datasets differ in quality, and technologies change quickly. Some numerical estimates differ within the paper, including internet access figures, making precise comparisons less dependable than the broader patterns described. Many reported opportunities come from pilot projects or individual cases, and findings from these two countries cannot represent every African farming system.

How AI Could Help Farmers and Food Supply Chains

Sensors, drones, satellite images and image recognition can help farmers identify pests, disease, nutrient problems and water stress before damage spreads. More targeted irrigation, fertiliser use and spraying could reduce waste and unnecessary chemical applications. Weather forecasts and early warnings could guide planting dates, crop choices and preparations for droughts, floods or pest outbreaks, with accuracy depending on reliable local data.

Digital platforms could connect growers with buyers, improve price information and help businesses anticipate demand, giving farmers better opportunities to sell their produce. Automated grading, food quality checks, improved storage and refrigerated transport could reduce losses between harvest and consumption. Traceability tools combining AI with connected sensors and digital records could make food safety problems easier to investigate, and nutrition applications could support healthier eating.

Mobile advisory services and chatbots could bring agricultural guidance to communities with limited access to extension workers. Credit assessments using satellite information and mobile data could help lenders consider farmers without conventional credit histories, creating possible routes to loans and insurance. Automated machinery, livestock monitoring and robotic harvesting offer further ways to manage farm work, with affordability and practical suitability determining their value.

Why Nigeria and South Africa Need Different Approaches

Nigeria's large smallholder farming sector offers considerable room to improve yields, market access and rural livelihoods through accessible digital services. Unreliable electricity, weak connectivity, fragmented agricultural records, expensive equipment and shortages of technical skills restrict adoption. Conflict, displacement, inflation, unpredictable rainfall and desertification create pressures that agricultural software cannot resolve on its own.

South Africa has stronger digital infrastructure, research capacity and commercial farming businesses that can use precision tools to improve established operations. The paper describes opportunities in drone monitoring, logistics, automation and irrigation management, particularly where scarce water makes efficient use essential. Poverty, unemployment, historical inequalities, rural connectivity gaps and electricity interruptions leave many households and smaller farmers outside those benefits.

Nigeria needs stronger foundations for widespread participation, and South Africa needs broader access to its existing technological advantages. The paper reports precision agriculture adoption below 10% in Nigeria and around 30–40% in South Africa, largely associated with commercial farming. These estimates describe an uneven starting point rather than evidence that buying AI equipment will deliver a particular increase in yields.

Making Agricultural AI Affordable, Fair and Sustainable

Expensive systems can deepen the divide between large businesses and farmers with limited resources, and biased credit algorithms can reject people with sparse digital records. Unclear data ownership, weak privacy protections, cybersecurity risks and dependence on outside technology companies can reduce farmers' control over their information and decisions. Tools designed around uniform industrial farming may encourage single-crop production, chemical dependence or practices poorly suited to local ecosystems.

The authors recommend investment in rural broadband, dependable electricity, renewable energy and shared computing infrastructure, supported by clear agricultural AI policies and accountable institutions. Better-connected databases covering weather, soils, harvests and markets would give forecasting and advisory tools a stronger foundation. Subsidies, affordable finance, farmer training and accessible mobile services could help smaller producers use technologies that currently sit beyond their reach.

Governments, researchers, technology businesses and farmers' organisations need to develop solutions together, with local knowledge shaping how tools work in practice. Nigeria's priorities include closing productivity gaps and expanding affordable services, and South Africa's include fairer access and careful management of scarce water. Progress should be judged by dependable access to nutritious food, stronger farmer livelihoods and healthier ecosystems, with locally developed AI supporting the wider sustainable development goals.