How AI can inclusively transform agri-food systems in Africa

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Editor's note:

This viewpoint is part of Foresight Africa 2024.

AI and other automation technologies are presenting game-changing opportunities for [Africa’s] smallholder farmers.

Advances in artificial intelligence (AI) will be the most significant contributor to the transformation of agri-food systems in Africa. OpenAI’s ChatGPT application exemplifies the rapid pace of advancement in AI capabilities in the last year alone. AI and other automation technologies are presenting game-changing opportunities for the continent’s smallholder farmers, particularly when delivered through low-tech delivery channels, in-person intermediary networks, and through partnerships with value chain stakeholders to subsidize costs.

A report by Genesis Analytics provides a sneak peek into this future. Data from sensors, satellites, and drones is enabling optimal use of land based on specific crop suitability. Automated systems, including irrigation, ensure efficient resource utilization. AI-enabled advisory services provide farmers with timely, tailored advice to boost yields and manage pests, reducing crop failure, spoilage, and bolstering food security. More accurate farming minimizes costs and environmental impact by using resources efficiently. Traceability tools reduce certification costs, broadening market access. AI-driven risk analysis facilitates access to crucial financial services like credit and insurance. The report identifies the types of solutions with existing pockets of adoption impacting smallholder farmers in Africa.

However, realizing these benefits broadly is far from automatic. Most of these solutions are concentrated in Kenya, South Africa, and Nigeria. Even where solutions do exist, smallholder farmers without access to the networks, hardware, and capital necessary to use these solutions will not benefit. There is a threat of larger farms, enabled by technology, outpacing smaller farms in productivity and endangering rural livelihoods. Gender disparities in technology adoption can exacerbate household inequalities, and concerns about data governance and potential labor displacement due to AI-enabled automation are real.

To navigate these challenges and harness AI’s potential inclusively, four areas must be prioritized:

  1. Building strong data and technology infrastructure: AI’s power lies in data. With businesses controlling much of this asset, creating incentives to share this data is crucial. By reducing the costs of on-farm technology like sensors and drones, governments can level the playing field. Developing agri-specific opensource software infrastructure can support AI tools being tailored to local African contexts at scale.
  2. Championing farmer-centric solutions: For AI to have a broad impact, solutions must be rooted in local contexts. This means tools in local languages, introduced through trusted human intermediaries. Empowering farmer cooperatives to participate in AI solution development and become procuring entities can boost AI adoption. Unlocking government demand for climatesmart digital extension advisory services will go a long way to addressing the financial sustainability of these solutions.
  3. Balancing innovation with demographic and environmental transitions: With a growing youth population, empowering young people in Africa to transition into new work opportunities in the AgTech value chain is urgent. Climate change necessitates eco-friendly AI solutions, with AgTechs taking responsibility for their environmental footprint.
  4. Upholding ethical standards in AI and data use: As with any nascent technology, ethical challenges are inevitable. Impact assessments can prevent biases, and participatory governance like data trusts ensure fair data use. Remedies for potential harms and specialized ethical assessment tools are essential. Emphasizing farmer-centric data governance, empowering organizations to support farmers, and establishing a regional AI lab can enhance AI model accuracy and accountability in African agriculture.


  • Footnotes
    1. Yang, C.. 2018. “High resolution satellite imaging sensors for precision agriculture.” Frontiers of Agricultural Science and Engineering Vol. 5 (issue 4).
    2. GSMA. 2018. “eFishery: Shaping the future of Indonesia’s aquaculture industry/”.
    3. Springer, E. et. al.. 2023. “Inclusive Digital Design Toolkit.”
    4. Digital Agri Hub. 2022. “Assessment of smart farming solutions for smallholders in low and middle-income countries.”
    5. Apollo Agriculture. 2019. “Increasing Food Security in Africa.”
    6. World Bank. 2020. “Scaling Up Disruptive Agricultural Technologies in Africa.”
    7. Ryan, M. 2019. “Ethics of Using AI and Big Data in Agriculture: The Case of a Large Agriculture Multinational.”