Harnessing Artificial Intelligence for Agricultural Transformation

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This report presents a comprehensive analysis of how AI can be responsibly deployed across agrifood systems, especially in low- and middle-income countries. It provides a roadmap of applications, requirements, and investment priorities focusing on ethical, inclusive, and scalable use.

• AI can transform agricultural production for smallholder farmers in low- and middle-income countries – helping feed the world, strengthening climate resilience, and easing work on the farm. But to achieve this, AI must be used where it truly adds value.
• AI can improve agrifood systems, but only with the right investments in infrastructure, governance, skills, and a focus on inclusion and ethics. This is essential for small-scale producers – who grow a third of the world’s food – to truly benefit.
• Governments, development partners and the private sector must collaborate and invest to realize AI's potential to increase productivity and advance climate adaptation and equity. No one player within the value chain can do this alone.

The report includes 60 AI use cases across the agrifood value chain, showing why they matter and how they can be adapted to different low- and middle-income country contexts. These include:

• Crops and livestock – accelerating research to find climate-resilient seeds and better breeding methods.
• Advisory and farm management – helping farmers make smarter decisions using AI for pest detection, precision farming, and real-time soil monitoring.
• Markets, distribution and logistics – improving market transparency and reducing spoilage with AI-enabled traceability, price forecasting, and smart contracts.
• Inclusive finance and risk mitigation – expanding financial access through alternative credit scoring and climate-indexed insurance models.
• Cross-cutting applications – supporting planning with tools such as synthetic data, agroecological zoning, and granular weather prediction.

Recommendations

Policy makers:

• Adopt national AI strategies inclusive of agriculture, with clear implementation pathways and budgets.
• Embed AI in AgriFood System Policy by linking it to resilience, climate adaptation, and nutrition security goals.
• Foster Open and Interoperable Data Ecosystems by supporting Agricultural Data Exchange Nodes and FAIR data principles.

Development institutions:

• Integrate digital public infrastructure and AI Investments in agriculture projects, ensuring that identity, payments, and data infrastructures are AI-ready.
• Support AI readiness assessments and policy diagnostics for low- and middle-income governments, especially in fragile or climate-vulnerable regions.
• Direct research funding toward building AI models with local institutions in low- and middle-income countries, focusing on crops, languages, and supply chains that global models often overlook