Artificial intelligence (AI) could play a significant role in strengthening food security in Nigeria and South Africa, although its potential impact will depend on differences in agricultural infrastructure, investment, digital readiness and government policies, according to a comparative study by researchers Lere Amusan and Adetayo Sowale.

The study finds that while AI could help Nigeria address low agricultural productivity, improve access to finance and equip smallholder farmers with better information, South Africa stands to benefit primarily through precision agriculture, improved resource management and more efficient agricultural supply chains.

The researchers emphasise that successful AI adoption across Africa will require approaches tailored to individual countries rather than a uniform technological model.

Different Agricultural Systems Require Different AI Strategies

Nigeria’s agricultural sector is dominated by smallholder farmers and contributes approximately 23% of national GDP, employing around 36% of the country’s workforce. However, the sector faces a significant productivity challenge, with actual agricultural yields falling more than 76% below their potential.

The study identifies AI as an opportunity to address these challenges through improved agricultural advice, predictive analytics, automated monitoring and greater access to financial services.

In South Africa, agriculture contributes approximately 2.5% of GDP and employs around 6% of the workforce. Its comparatively advanced commercial farming sector, mechanisation and telecommunications infrastructure create opportunities for AI to improve operational efficiency.

Applications include automated irrigation, drone-based crop monitoring, satellite imaging and predictive systems that help farmers identify pests, diseases and moisture stress before they cause significant crop losses.

South Africa Leads in Precision Agriculture Adoption

The study estimates that precision agriculture adoption in South Africa stands between 30% and 40%, compared with less than 10% in Nigeria.

South Africa’s agritech innovation ecosystem is also assessed as more mature, with a score of 75%, compared with 45% for Nigeria.

Companies such as Aerobotics demonstrate how AI and aerial imaging technologies can help commercial farmers identify early signs of pest infestations, plant diseases and water stress.

Nigeria, meanwhile, has significant untapped potential because of its agricultural productivity gap. The researchers estimate that the country’s potential for AI-driven productivity improvements could reach 80%, compared with 75% in South Africa.

However, realising this potential will require more affordable technologies and wider access to digital infrastructure, particularly among smallholder farming communities.

AI Could Improve Market Access and Agricultural Financing

Beyond improving farm productivity, AI could help address inefficiencies in agricultural supply chains and financial services.

In Nigeria, smallholder farmers frequently face fragmented markets, limited access to reliable pricing information and dependence on intermediaries. Digital platforms such as Fodlocker demonstrate how machine-learning-based demand forecasting can connect farmers more directly with retailers.

AI-powered credit assessment systems could also expand financing opportunities by analysing satellite imagery and mobile transaction records to assess farmers who lack conventional collateral.

The study identifies a potential financial inclusion opportunity of approximately 70% through these technologies.

In South Africa, AI applications are more focused on improving existing commercial systems. Platforms such as Desert Green and Mezzanine are cited in connection with agricultural distribution, logistics coordination and demand forecasting.

However, the researchers caution that automated lending systems could reproduce existing financial inequalities by excluding farmers with limited digital records, inadequate documentation or insecure land tenure.

Climate Change Creates Opportunities for AI-Driven Agriculture

Climate resilience represents another important area where AI could strengthen food security in both countries.

The study assigns both Nigeria and South Africa a climate vulnerability index score of 75%, highlighting their exposure to extreme weather, unpredictable rainfall and environmental degradation.

However, their adaptation priorities differ.

South Africa faces severe water scarcity, particularly in semi-arid agricultural regions. The researchers assign the country a smart irrigation demand intensity score of 80%, reflecting the importance of technologies that optimise water consumption through precision irrigation and machine-learning systems.

Nigeria, where irrigation infrastructure remains underdeveloped, has an estimated intelligent water management capacity of 50%. Its priorities include flood warnings, seasonal drought forecasting and localised soil monitoring.

According to the researchers, these applications could also support more sustainable agricultural practices by reducing unnecessary fertiliser and pesticide use while protecting soil health and biodiversity.

Infrastructure and Inequality Remain Major Barriers

Despite the opportunities, the study identifies significant differences in the countries’ readiness to implement AI technologies.

South Africa has an estimated electricity access rate of 87.7% and internet penetration of between 74% and 78%. Nigeria records electricity access of 61.2% and internet penetration ranging from 41% to 53%.

Digital skills capacity is also estimated at 70% in South Africa, compared with 40% in Nigeria.

The researchers further assess national AI governance readiness at 60–65% for South Africa and 35–40% for Nigeria.

In Nigeria, unreliable electricity, rural connectivity challenges, high hardware import costs and fragmented agricultural data systems limit the effectiveness of advanced digital technologies.

In South Africa, the principal challenge is affordability and unequal access. High acquisition costs for autonomous machinery, multispectral sensors and automated processing equipment risk widening the gap between large commercial farms and under-resourced smallholder producers.

South Africa Scores Higher in Overall AI Food Security Prospects

The comparative analysis gives South Africa an overall Global Food Security Index score of 65.5 and an AI food security prospect score of 70%.

Nigeria records a Global Food Security Index score of 57 and an AI food security prospect score of 57%.

The stronger performance of South Africa reflects its comparatively advanced digital infrastructure, institutional coordination, research capabilities and commercial agricultural systems.

Nevertheless, the researchers argue that technological adoption alone cannot eliminate rural poverty or address structural inequalities in agricultural markets.

They identify investment in rural roads, secure land tenure, agricultural extension services and decentralised solar energy as essential complements to AI deployment.

Inclusive Investment Needed to Unlock AI’s Agricultural Potential

The study concludes that AI should not be viewed as a standalone solution to food insecurity in Africa.

Instead, its contribution will depend on targeted public investment, effective data governance, accessible digital infrastructure and technologies designed around the needs of different farming communities.

The researchers also acknowledge limitations in their findings, as the investigation relied on secondary research and published institutional data rather than direct fieldwork with farmers, technology developers and policymakers.

Ultimately, Nigeria and South Africa demonstrate two different pathways for agricultural digitalisation. While Nigeria could use AI to expand basic productivity and financial inclusion, South Africa has greater opportunities to optimise established commercial farming operations.

For both countries, ensuring that technological gains reach smallholder farmers and support environmental sustainability will be critical to translating AI innovation into lasting improvements in food security.