The transformative potential of artificial intelligence (AI) in agriculture is increasingly recognized, yet most large language models (LLMs) remain inaccessible to small-scale producers (SSPs) in Africa and India due to high computational requirements, limited localization, and infrastructure constraints. Artificial intelligence in agriculture offers significant promise for smallholder farmers, agricultural extension systems, digital agriculture transformation, and agri-food system modernization; however, a widening gap persists between AI capability and real-world accessibility in low- and middle-income countries (LMICs). Most LLMs are designed for high-bandwidth environments, trained on non-representative datasets that poorly reflect African agriculture and Indian agriculture systems, and priced beyond the reach of agricultural extension agents, farmer organizations, and cooperatives serving SSPs, resulting in structural exclusion of smallholder farmers.
This paper examines the development and deployment of affordable, locally fine-tuned agricultural large language models (Agri-LLMs) for smallholder agriculture, agricultural advisory services, and digital extension systems through multi-stakeholder public-private partnerships integrated within agricultural extension systems and digital agriculture ecosystems. It reframes AI localization in agriculture as a systems-level challenge requiring alignment across AI model architecture, agricultural data governance, sustainable financing models, and policy frameworks.
It further explores localized, low-power, cost-efficient AI systems supporting climate-smart agriculture, precision agriculture, and farmer decision-support systems tailored to linguistic, cultural, and agronomic diversity in Africa and India, drawing on India’s Digital Public Infrastructure (DPI) for agriculture and Africa’s emerging agri-tech and AI innovation ecosystems.
