thianchai sitthikongsak/ Getty Images Globally, agriculture faces mounting pressures. These are driven by climate change, land degradation, labour shortages, supply chain disruptions and the demand for food from a growing population.
At the same time, productivity is uneven. For example, maize yields in the US often exceed 10 tons per hectare. These high yields are driven by mechanisation, improved seed varieties, irrigation and efficient input use, supported increasingly by precision agriculture technologies. In contrast, yields in many parts of sub-Saharan Africa remain around 2-3 tons per hectare. This reflects constraints like limited access to inputs, reliance on rain-fed systems and weaker infrastructure and institutional support.
Smallholder farmers make up around 80% of farmers in developing countries. They often struggle with low yields due to limited access to key agricultural inputs such as improved seeds, fertilisers and agrochemicals (herbicides and pesticides). They are less likely to rely on irrigation and farm mechanisation. They also have high vulnerability to climate shocks.
Conventional farming practices, including reliance on rain-fed agriculture, the use of low-yielding local seed varieties, sub-optimal input application and heavy dependence on manual labour, are increasingly insufficient to meet the demands of 21st-century food systems.
In recent years, the use of artificial intelligence (AI) tools has been shown to improve input-output efficiency and enable real-time monitoring of crops and livestock. They’ve been shown to conserve soil and water resources, and reduce post-harvest losses particularly in technologically advanced agricultural systems in the US, China and Europe.
We have over 15 years of scholarship in applied economics, development, resource economics and agricultural economics, including technology adoption and sustainable agricultural systems. Our recent study compared AI adoption in agriculture between developed and developing countries.
We examined how artificial intelligence is accessed and used across different regions. Evidence from technologically advanced economies such as Europe, the US, Australia and Japan was analysed alongside studies from Africa, South Asia, Latin America and other low- and middle-income regions.
Our main finding was that AI has strong potential to improve agricultural productivity and resilience. But this potential depends on supportive policies, reliable infrastructure and equitable access. Without these, the technology could reinforce existing inequalities rather than reduce them.
patterns of AI adoption: including the extent of uptake across regions, and types of AI applications used in agriculture (such as precision farming, disease detection, yield prediction, and smart irrigation)
levels of infrastructural readiness: including the availability of electricity, broadband connectivity, digital literacy support, data management systems, smart devices, and extension or technical support services necessary for effective AI adoption
key concerns around ethics and data governance: including data ownership, privacy and security, informed consent, algorithmic bias, transparency, accountability, and equitable access to AI-driven agricultural technologies.
We also explored how national policies are responding to emerging risks. These include data privacy breaches, cybersecurity vulnerabilities, labour displacement, and unequal access to AI-enabled agricultural technologies. This approach allowed us to capture both global trends and region-specific realities.
AI is increasingly shaping agriculture in developed countries. Technologies such as precision farming tools are helping improve fertiliser use, irrigation, yield prediction and pest management, while also supporting more efficient resource use and greater resilience to climate variability.
Digital infrastructure: In many developed countries, reliable internet, satellite systems, cloud platforms and connected sensors enable continuous data collection and analysis. This supports real-time farm decisions and the seamless use of precision agriculture technologies.
Strong institutional support: This has enabled rapid uptake of innovations in agriculture. The support includes established governance frameworks that provide operational clarity on data privacy, transparency and accountability. This enabled more responsible technological innovation.
Reliable electricity: This is essential for AI-driven agriculture. It ensures the continuous operation of digital systems and technologies such as sensors, automated irrigation, drones, and data platforms.
But we found that AI adoption remains limited in developing countries, where smallholder farmers dominate food production. The limiting factors included:
The digital divide: We identified this as the biggest barrier. Farmers often lack stable internet connectivity, affordable devices, or sufficient digital literacy.
Electricity: Shortages hinder the adoption and effective use of AI in agriculture by disrupting the operation of digital tools and infrastructure. These are required for data collection, processing and communication.
Cost: High cost of AI tools and a lack of digital literacy to engage with AI tools effectively.
