Artificial Intelligence for Agricultural Transformation

Source:  WB

Subject:   Agriculture

Context: A new World Bank–led report “Harnessing Artificial Intelligence for Agricultural Transformation” outlines how AI can be responsibly scaled across agrifood systems, especially in low- and middle-income countries.

About Artificial Intelligence for Agricultural Transformation:

Current trends of AI in agriculture:

• Shift to GenAI & multimodal AI: New models combine text, images, satellite data and sensor feeds to give natural-language, local-language advisories and predictive insights for farmers.
• Systems-level adoption: AI is now used across the value chain—crop discovery, advisory, insurance, logistics and market intelligence—rather than in isolated pilots.
• Rapid growth in investments: The AI-in-agriculture market (~US$1.5 bn in 2023) is projected to reach about US$10.2 bn by 2032.
• LMIC-focused experiments: Numerous projects in Africa and Asia now use AI for hyperlocal weather, pest diagnosis, and input optimisation tailored to smallholders.
• “Small AI” on phones: Lightweight models that run on basic smartphones or offline devices are emerging to serve farmers in low-connectivity environments.

Opportunities of AI in agriculture:

• Higher yields & input efficiency: AI-based precision farming, irrigation, and fertilizer tools can cut chemical use (up to ~95% in some drone-based pilots) while raising yields by 20–30%.
• Climate resilience: AI helps breed climate-resilient varieties, model climate risks, and plan cropping patterns using high-resolution agro-ecological and weather data.
• Better incomes & market access: Projects like Saagu Baagu in India show AI advisories can raise farmer income per acre, improve quality and reduce input costs, while tools like Hello Tractor optimise machinery access.
• Inclusive finance & risk mitigation: Alternative credit scoring, AI-based micro-insurance and climate-indexed products can expand formal finance to previously unbanked smallholders.
• Smarter public policy: Governments can use AI for early-warning systems, yield and price forecasting, and targeted subsidies, improving food-security planning and resource allocation.

Initiatives already taken:

• Global AI roadmap by World Bank & partners: The report itself, with 60 use cases, gives a structured roadmap for LMICs on applications, governance and investments.
• Research institutions using AI: IRRI, CIMMYT and others use ML and computer vision to speed up phenotyping and genebank screening, tripling the number of accessions screened while cutting costs.
• Data coalitions & exchanges: Ethiopia’s “Coalition of the Willing” and India’s Agricultural Data Exchange (ADeX) create shared data layers to train local AI models while protecting sovereignty.
• Public–private digital platforms: Initiatives like the Agriculture Information Exchange Platform (AIEP) in Kenya and Bihar pilot GenAI advisory tools in multiple local languages for tens of thousands of users.

Key challenges associated:

• Digital divide & infrastructure gaps: Only a small share of rural populations in regions like Sub-Saharan Africa have reliable internet and electricity, limiting AI deployment and real-time services.
• Data bias and scarcity: Most training data comes from high-income regions; local crops, soils and indigenous practices are under-represented, leading to biased or irrelevant recommendations.
• Low human capital & trust: Many farmers, especially women and older farmers, lack digital skills; distrust of automated advice and language barriers can slow adoption.
• Weak governance & regulation: Clear rules on data ownership, privacy, algorithmic transparency and liability for AI errors are still evolving in most LMICs.
• Risk of exclusion & concentration: Without safeguards, AI could deepen inequalities, create vendor lock-in, or favour large agribusinesses over smallholders in access to insights, finance and markets.

Way ahead:

• Adopt national AI strategies with agri focus: Countries should explicitly integrate agriculture into AI strategies, with budgets, timelines and links to food-security, climate and nutrition goals.
• Invest in digital public infrastructure & connectivity: Expand rural broadband, green data centres, and interoperable registries so that AI tools can plug into common, publicly governed rails.
• Build inclusive data ecosystems: Support Agricultural Data Exchange Nodes and FAIR/open data principles so local data (crops, soils, weather, practices) can safely train context-specific models.
• Strengthen skills and extension systems: Train farmers, extension workers and agri-startups in digital and AI literacy, using local-language, multimodal interfaces and train-the-trainer models.
• Create robust governance & ethical frameworks: Enact laws on data rights, transparency, environmental standards and accountability for AI, using sandboxes and participatory policy-making.

Conclusion:

AI has the potential to significantly boost productivity, resilience, and efficiency across agrifood systems. However, to realise these gains, countries must bridge digital infrastructure gaps, strengthen data ecosystems, build farmer-level capacities, and ensure robust governance. Used responsibly and inclusively, AI can complement wider agricultural reforms and support long-term food security, income growth, and environmental sustainability.