How AI is transforming extension services for precision smallholder farming

By Shalini Gakhar and Niyati Singaraju (International Rice Research Institute)

Agriculture is at a transformative crossroads, where cutting-edge technology meets traditional farming practices. Artificial intelligence (AI) is emerging as a game-changer, enabling smarter, more efficient, and sustainable farming systems. By integrating real-time data from sources like IoT sensors, drones, and advanced computer vision technologies, AI empowers farmers to make informed decisions tailored to the specific needs of their crops and the environment (FAO, 2024). This precision-driven approach is not just a technological leap: it is an essential response to the twin challenges of global food insecurity and climate change.

Recognizing these challenges, global initiatives are working to create pathways for equitable and effective digital transformation in agriculture. One such effort is the Stakeholder Consultation Workshop organized by International Rice Research Institute (IRRI) and International Maize and Wheat Improvement Center (CIMMYT) under the CGIAR Initiative on Digital Innovation, in Bhubaneswar, Odisha from 29-30 November 2024. This event brought together a diverse group of experts to explore the opportunities and challenges of AI-based agricultural extension systems and to develop pathways for equitable and effective digital transformation.

Collaborative Pathways for Digital Transformation though AI

The workshop showcased how AI is already reshaping farming practices. Predictive analytics, for instance, can provide farmers with early warnings about weather patterns, while AI-driven tools can diagnose crop diseases using smartphone cameras and offer customized recommendations. Tools like the Rice Crop Manager (RCM) from IRRI and Wadhwani AI’s pest surveillance system have already demonstrated their ability to improve productivity and mitigate risks, making farming more adaptive and resilient to challenges such as climate change.

Challenges: The Digital Divide and Ethical Concerns

Despite its transformative potential, hurdles remain to integrate AI in agriculture in the Global South. One significant challenge is the digital divide. Many smallholder farmers lack access to the tools, infrastructure, and digital literacy needed to benefit from these technologies. For women and marginalized groups, the barriers are often even higher. These disparities highlight the importance of ensuring that AI technologies are accessible and affordable for all farmers.

Ethical concerns also demand attention. Questions about data privacy, equity, and potential biases in AI algorithms remain critical. Poorly designed systems risk reinforcing existing inequalities, particularly if marginalized communities are excluded from the data collection and design processes. The workshop emphasized the need for transparent and fair governance frameworks that involve farmers at every stage of development, ensuring that AI technologies serve the interests of all stakeholders.

Collaboration and Ecosystem Building

Collaboration emerged as a key theme at the workshop. No single organization can address these challenges alone. Governments, private companies, non-profits, and farmer groups must work together to create a supportive ecosystem for digital innovation. One promising example is Jugalbandi, a public-private partnership model that combines government oversight with private sector efficiency to scale digital solutions effectively.

Scaling Digital Solutions

The workshop also underscored the importance of scaling AI tools beyond pilot projects. While many innovations have shown success on a small scale, expanding their reach to millions of farmers requires careful planning and collaboration. Human-centered design, which prioritizes the experiences and needs of farmers, is crucial. By involving farmers in every step of the development process, AI tools can become more user-friendly and relevant to local contexts, fostering trust and wider adoption.

Building Sustainable Business Models

Sustainability was another pressing concern. Many AI-driven tools rely on short-term funding, raising questions about their long-term viability. The workshop explored diverse business models, ranging from direct-to-farmer approaches to partnerships with Farmer Producer Organizations (FPOs) and governments. Successful models must strike a balance between affordability for farmers and financial sustainability, ensuring that these innovations can scale without losing their accessibility.

The Way Forward

As the workshop concluded, participants identified several priorities for the future integration of AI in agriculture. Bridging the digital divide remains paramount, requiring investments in infrastructure like broadband connectivity and affordable devices. Equally important are digital literacy and capacity-building programs to help farmers use AI tools effectively. Initiatives such as IRRI Education and the ICT Learning Network Academy are already making headway in this area.

Ethical considerations must guide every step of the process. Transparent data governance, fairness in AI algorithms, and the inclusion of marginalized voices are essential to ensuring equitable outcomes. Public-private partnerships will also play a crucial role in scaling AI innovations and sustaining their impact.

AI has the power to revolutionize agriculture, but its success depends on inclusivity, collaboration, and ethical governance. By addressing challenges like the digital divide and fostering strong partnerships, stakeholders can unlock AI’s full potential to create a resilient, sustainable, and equitable agrifood system. The journey is complex, but the rewards—smarter farming, better resource management, and improved livelihoods for farmers worldwide—are well worth the effort.