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By: Nukanah Kollie
Across Africa’s farmlands, a quiet but powerful revolution is unfolding, one driven not by tractors or fertilizers alone, but by artificial intelligence (AI). As climate shocks intensify and food systems face mounting pressure, AI is emerging as a critical tool, helping bridge the gap between scientific research and the daily realities of smallholder farmers.
From listening to farmers’ voices to diagnosing crop health through images, AI is reshaping how agricultural research connects with the people who depend on it most. According to the Food and Agriculture Organization (FAO), more than 60 percent of Africa’s population depends on agriculture for livelihoods, yet the sector continues to suffer from low productivity, climate vulnerability, and limited access to timely data. AI-driven solutions are now beginning to change that narrative.
Speaking during a recent discussion on digital agriculture recently in Nairobi, Kenya, Stephen Mutuvi, a scientist with the Consultative Group on International Agricultural Research (CGIAR) based in Arusha, Tanzania, explained how AI tools are already transforming farmers’ lives across East Africa, with clear plans to scale these innovations to West Africa, including Ghana.
One of the most impactful AI applications currently in use focuses on speech-to-text technology, designed to overcome one of agriculture’s most persistent barriers: literacy. In many rural African communities, adult literacy rates remain below 65 percent, particularly among women and older farmers. Mutuvi noted that while not every farmer can read or write, every farmer can speak.
Instead of relying on written surveys, researchers now deploy trained community-based enumerators, often recent university graduates familiar with local languages, farming systems, and terrain to record farmers as they speak freely about their crops and farming experiences. This approach has significantly expanded participation, especially among women and elderly farmers who were previously excluded from conventional data collection methods.
Farmers share their preferences and observations in their own words, explaining why certain crop varieties perform better than others, how they respond to drought or pests, and which traits matter most to them. A farmer might explain that a variety grows faster, withstands dry weather, or produces better yields under poor soil conditions. AI-powered speech-to-text models then transcribe these responses directly into digital text, even when spoken in local languages. From there, natural language processing (NLP) tools, similar to ChatGPT, analyze thousands of responses to identify patterns, trends, and practical insights at a scale that would be impossible through manual analysis.
The real value of this data lies in how it feeds back into the agricultural research system. Once analyzed, farmers’ feedback is shared with plant breeders and researchers working at research stations across East Africa, including Kenya and Tanzania. These insights help breeders understand why a variety succeeds or fails in real field conditions, rather than only under controlled laboratory or station environments. If farmers consistently report that a variety performs poorly under dry conditions, breeders can use that information to improve drought tolerance in future seed development.
This farmer-centered feedback loop bridges a long-standing gap between research stations and farmers’ fields, ensuring that new seed varieties are practical, resilient, and economically viable. According to CGIAR estimates, improved, climate-resilient seed varieties can increase smallholder yields by 20 to 40 percent, directly translating into higher incomes and improved food security.
Beyond listening to farmers’ voices, AI is also helping researchers and farmers see problems early. Another application being tested across East Africa uses image recognition technology. By simply taking a photo of a crop with a smartphone, AI systems can determine whether a plant is healthy or showing signs of disease, pest damage, or nutrient stress. Early trials show that such tools can reduce crop losses by enabling faster interventions, particularly for diseases that spread rapidly if left undetected.
According to Mutuvi, this simple action enables quicker diagnosis and faster decision-making, reducing losses and improving productivity. He emphasized that the same technology can work in any African country, provided local conditions such as terrain, climate, and crop types are taken into account.
While the tools were developed and refined in East Africa, expansion is already underway. Pilot activities exist in Nigeria and Senegal, and by mid-2026, the research team plans to scale further across West Africa, a region where agriculture employs nearly 70 percent of the workforce.
Ghana is firmly on that expansion roadmap. Mutuvi explained that while the core technology remains the same, implementation must be adapted to each country’s agricultural landscape. Differences in terrain, farming systems, connectivity, and infrastructure require adjustments, just as researchers had to fine-tune their approach when moving from Tanzania’s flatter landscapes to more varied terrain in countries like Uganda. These lessons are now shaping future scaling strategies.
Ghana’s growing digital ecosystem also offers a strategic advantage, including the presence of Google’s research office in Accra, which strengthens opportunities for collaboration, local technical support, and innovation partnerships.
At its core, AI’s contribution to agriculture lies in speed, scale, and accuracy. By enabling faster data collection and analysis, AI allows researchers, breeders, policymakers, and farmers to make more informed decisions. For farmers, this means quicker responses to climate shocks, better access to improved seed varieties, and stronger, more resilient livelihoods.
As West Africa prepares to adopt these tools, one thing is becoming increasingly clear: AI is no longer a distant or abstract concept in African agriculture. It is emerging as a practical, farmer-centered partner, one that listens, learns, and responds to the realities of life in the field, while opening new pathways toward food security and sustainable development across the continent.
