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.
The potential and the gaps
Our review examined:
- 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.
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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.
The factors that made this possible included:
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.
Limited access to credit: Without sufficient financial capacity, farmers struggle to invest in digital technologies. They cannot afford the upfront purchase costs, installation expenses, or ongoing maintenance and subscription fees required to use AI tools effectively.
AI downsides
We also identified two factors that undermine the adoption of AI in Africa and other developing countries.
First, many AI models are not well-suited to developing country contexts. Tools trained on data from industrialised farming systems often perform poorly in local environments. It leads to biased or inaccurate recommendations and increases risks for vulnerable farmers.
For example, an AI-based yield prediction or pest detection model trained on large-scale monoculture farms in the US or the Netherlands could generate unreliable recommendations when applied to African smallholder farms characterised by mixed cropping, irregular input use, rain-fed agriculture and highly heterogeneous soil conditions.
Second, there are ethical concerns around AI use, particularly the lack of clarity on data ownership and privacy. Weak data governance is most pronounced in developing regions. Farmers often have little control over how their data is collected, used or monetised.
These challenges are not evenly distributed. But the risks are more pronounced in low-income regions, where regulatory systems are weaker, and smallholders have fewer resources to manage technological change.
Without appropriate safeguards, AI could reinforce disparities already embedded in global food systems. It also risks deepening existing inequalities, limiting its contribution to sustainable development and food security.
Way forward
AI could transform agriculture in Africa and other developing economies, but without the right policies, it may deepen inequality instead.
The priority is to fix the foundations. Reliable electricity, internet access, and affordable digital tools are essential. Without these, AI will remain out of reach for most smallholder farmers. Access to finance, training, and locally relevant data systems will also be critical.
Adoption should be gradual, starting with simple tools like advanced mobile advisory services before scaling up.
AI must be inclusive and farmer-centred. Done right, it can strengthen food systems. Done poorly, it risks leaving the most vulnerable further behind.
- is an Associate Professor, University of Maryland Eastern Shore; North-West University
- is a Senior Lecturer at Oyo State College of Agriculture and Technology, Igboora, Nigeria and Extraordinary Senior Lecturer at Indigenous Knowledge Systems Centre, North-West University, Mafikeng, South Africa, North-West University
- This article first appeared in The Conversation
