Photo: Anna Medvedeva, AgroXXI.ru
“Crop productivity is uneven. For example, corn yields in the United States often exceed 10 tons/ha. These high yields are due to mechanization, improved seed varieties, irrigation and efficient use of resources, increasingly facilitated by precision farming technologies. In contrast, yields in many regions of sub-Saharan Africa remain at around 2-3 t/ha. This reflects constraints such as limited access to resources, dependence on rainfed (non-irrigated, LP) farming, and weak infrastructure and institutional support,” Abiodun Olusola Omotayo and Abib Babatunde Omotoso in an article published by The Conversation.
Smallholder farmers make up about 80% of the rural population in developing countries. They often experience low yields due to limited access to basic agricultural inputs such as improved seeds, fertilizers and agrochemicals (pesticides). They are less likely to use irrigation and agricultural mechanization. They are also highly vulnerable to climatic shocks.
In recent years, the use of artificial intelligence (AI) tools has demonstrated the ability to improve cost-benefit efficiency and provide real-time monitoring of plant and animal health. Smart applications for farmers are helping to conserve soil and water resources and reduce post-harvest losses, especially in technologically advanced agricultural systems in the US, China and Europe.
“We have more than 15 years of research in applied economics, development, resource economics and agricultural economics, including technology adoption and sustainable agricultural systems. In our recent study, we compared the adoption of AI in agriculture in developed and developing countries. We examined how artificial intelligence is used and applied in different regions. Data from technologically advanced economies such as Europe, the US, Australia, and Japan were analyzed, as well as studies from Africa, South Asia, Latin America, and other low- and middle-income regions. Our main conclusion is that AI has significant potential to improve agricultural productivity and sustainability. However, this potential depends on favorable policies, robust infrastructure, and equitable access. Without these, the technology may exacerbate existing inequalities rather than reduce them,” the authors of the paper note.
Key barriers to the effective application of AI in developing countries
AI adoption remains limited in developing countries where food production is largely centered on smallholder farmers. Limiting factors include:
- Digital divide. Biggest barrier. Farmers often lack a stable internet connection, affordable devices, or sufficient digital literacy.
- Electricity. Electricity shortages hinder the adoption and effective use of AI in agriculture by disrupting the digital tools and infrastructure needed to collect, process and transmit data.
- Cost. High cost of AI tools and insufficient digital literacy to effectively utilize these tools.
- Limited access to credit. Due to lack of financial capacity, farmers find it difficult to invest in digital technologies. They cannot afford the initial costs of purchase, installation, and ongoing maintenance and subscription fees required to effectively utilize AI tools.
Researchers have also identified two factors that hinder the adoption of AI in Africa and other developing countries.
First, many agricultural AI models are ill-suited to developing country contexts. Tools trained on data from industrialized agricultural systems often perform poorly in local conditions. This 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 monoculture farms in the United States or the Netherlands may produce unreliable recommendations when applied in Africa.
Second, there are ethical issues associated with the use of AI, in particular the lack of clarity around 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 monetized.
According to the experts mentioned, the priority is to strengthen the basics: reliable electricity, internet access, access to finance, access to training. And affordable digital tools.
