Artificial intelligence is emerging as a potential tool for addressing some of Africa’s long-standing agricultural challenges, from crop disease and pest management to weather forecasting, fertiliser use and access to agricultural advice.

For a continent facing shortages of agricultural scientists and extension officers, AI could help extend technical knowledge to farmers who have limited access to specialist support.

One of the most practical applications is crop disease detection. A farmer with a smartphone could photograph a plant leaf and submit the image to an AI-powered application. The system can analyse the image against patterns and information in its database and identify possible diseases or nutrient deficiencies.

This could be particularly valuable in regions where pests and diseases cause significant production losses. The Food and Agriculture Organisation estimates that between 30% and 40% of crop production in Africa can be lost to pests and diseases, creating a major challenge for food security.

AI could also help farmers respond to problems before they become severe by analysing weather conditions, historical crop information and other agricultural data to identify potential disease or pest outbreaks.

Turning Weather Data Into Farm Decisions

Weather forecasting is another area where AI could provide practical support.

While national meteorological agencies already produce weather forecasts, farmers often need more specific guidance on how those forecasts should influence their farming decisions. AI systems can combine local weather information with soil conditions and historical crop data to provide recommendations on planting, irrigation and harvesting.

Similar technology could support soil and fertiliser management. Rather than applying the same amount of fertiliser across an entire field, AI-powered systems could estimate nutrient requirements based on available information about the soil and crop.

This could help farmers use fertiliser more efficiently while potentially reducing input costs and improving productivity.

AI also has applications beyond the farm. Market intelligence systems can analyse prices and market trends to help farmers identify where and when to sell their produce. Digital agricultural advisers delivered through WhatsApp, SMS, voice calls or local-language interfaces could also expand access to extension services.

Building African Solutions

The growing use of AI in agriculture raises an important question for Africa: should countries primarily adopt technologies developed elsewhere, or develop solutions suited to their own agricultural conditions?

Developing African AI does not necessarily mean creating every component of the technology from scratch. African researchers and entrepreneurs can adapt existing technologies and train systems using local agricultural data, crops, languages and farming conditions.

Tanzania already provides an example through Kilimo AI, developed by computer scientist Dr Neema Mduma of the Nelson Mandela African Institute of Science and Technology in Arusha.

The platform can analyse images of crops to identify diseases and provide recommendations. A farmer can photograph a plant and submit the image for analysis, demonstrating how AI can be adapted to address specific agricultural challenges.

However, technology alone will not solve Africa’s agricultural productivity gap. Farmers also need reliable connectivity, affordable smartphones and data, electricity, quality inputs, financial services and access to markets.

There are also concerns about how greater automation could affect agricultural employment and extension services. These challenges make it important for AI adoption to complement farmers and agricultural professionals rather than simply replace human expertise.

For African agriculture, the significance of AI may ultimately depend less on how advanced the technology becomes and more on whether it can be made affordable, accessible and relevant to the millions of farmers who need practical information to improve production.