By Stuart Kerr, Technology Correspondent
Published: 27 July 2025
Last Updated: 27 July 2025
Contact: liveaiwire@gmail.com | Twitter: @LiveAIWire
Author Bio: About Stuart Kerr
From satellite-powered crop analysis to predictive yield modelling, artificial intelligence is increasingly being seen as a lifeline for the world’s most pressing food crises. But while AI offers powerful tools for optimising agriculture, critics warn that it may deepen the very inequalities it promises to solve.
Digital Tools, Real-World Impact
In the fields of rural India and sub-Saharan Africa, smallholder farmers are already using AI-powered mobile apps to determine when to plant, irrigate, and harvest. These tools, such as those deployed under the AIM for Scale initiative, combine real-time weather data with soil diagnostics to help farmers make more informed decisions. The result? Increased yields, reduced risk, and improved financial resilience.
As Reuters reports, these technologies are helping empower smallholder farmers with real-time insights and actionable data. However, many of those most in need still lack access to smartphones, mobile data, or electricity. This digital divide threatens to create an AI-fuelled food hierarchy, where data-rich regions thrive while vulnerable populations fall further behind.
Supply Chains on Autopilot
AI's reach extends far beyond the field. In sprawling distribution networks, algorithms are being deployed to forecast demand, reduce waste, and optimise cold-chain logistics. In one instance, AI-based tracking systems helped reduce food spoilage during transit by over 30%.
A recent piece in LiveAIWire titled "Supply Chain Intelligence" detailed how predictive analytics are being used to balance global supply with local demand. This data-driven agility is especially critical in fragile economies where even slight disruptions can cause severe food shortages.
The Climate Conundrum
AI also holds promise in tackling agriculture's intersection with climate change. From satellite imaging that monitors deforestation to models predicting the effects of drought, these technologies are crucial to adaptation strategies.
The United Nations University outlines this intersection in its article, emphasising how AI can help address Sustainable Development Goal 2: Zero Hunger. The institution also highlights how inclusive AI policy frameworks can promote food equity, reduce emissions, and build long-term resilience.
The Risk of Algorithmic Blind Spots
But the story isn’t all utopian. As Fast Company notes, most AI tools are trained on datasets that skew heavily toward developed nations. That means models optimised for American corn farms may fail when applied to Kenyan maize.
Furthermore, many AI projects in agriculture are funded by large tech firms whose commercial interests may not align with food sovereignty. If left unchecked, these interventions risk disempowering traditional farming knowledge and concentrating food governance in the hands of a few.
Building Equitable Algorithms
One solution lies in democratising AI—training models on local data, developed in collaboration with communities rather than imposed from above. The UNU's PDF report on agrifood resilience calls for decentralised infrastructure and public-sector AI designed for inclusivity.
Further research, such as "AI in Agriculture: Feeding the Future" and "AI in Farming", reinforces the need for regionalised, transparent systems that elevate rather than replace local expertise.
The UNU’s 2022 GEOC Report also emphasises how climate-neutral AI frameworks can reduce both emissions and food waste, provided policies are designed with ethical safeguards.
Finally, the Financial Times contributes to this broader conversation with its Tech for Growth Forum 2025 article, examining AI’s role in supporting climate-smart agriculture and fair food distribution in the Global South.
Conclusion: Promise vs. Power
AI has the potential to revolutionise how we grow, transport, and share food. But realising that promise requires more than just clever code. It demands global cooperation, inclusive datasets, and governance structures that prioritise people over profit.
If AI is to feed the world, it must first learn to listen to it.
About the Author
Stuart Kerr is the Technology Correspondent for LiveAIWire. He writes about artificial intelligence, ethics, and how technology is reshaping everyday life. Read more.