Here Is Our AI Plan to Reach 2.5 Million Rwandan Farmers
Summarized by DistantNews. Read the original for the full story.
At a glance
- Rwanda’s ICT and Innovation Minister Paula Ingabire said artificial intelligence could help farmers make better decisions about crops, disease, financing and markets.
- She cited tools in Kenya and Zambia that use images, satellite data and machine learning to diagnose crop disease and connect farmers with credit and farm inputs.
- Rwanda’s plan aims to bring AI-supported agricultural services to 2.5 million farmers.
For farmers making decisions about what to plant, when to sow and how much fertilizer to use, information often arrives too late or not at all. Rwanda’s ICT and Innovation Minister Paula Ingabire says artificial intelligence could put better advice directly in the hands of farmers and agronomists.
Speaking at the Africa Food Systems Forum in Kigali, Ingabire said AI should not replace farmers or agricultural experts. Instead, it could give farmers access to agronomists, weather specialists and financial advisers in languages they understand.
She pointed to Kenya’s PlantVillage Nuru as an example. Farmers can use a smartphone to photograph a cassava leaf and check whether the plant shows signs of disease. CGIAR research found that the technology could diagnose cassava disease symptoms more accurately than farmers and agricultural extension agents.
It is not about replacing a farmer. It is not about replacing agronomists. It is really about putting better intelligence in the hands of both farmers and agronomists.
Ingabire also cited Apollo in Zambia, which combines remote sensing, satellite imagery and machine-learning models. The system helps smallholder farmers who lack credit histories connect with agricultural inputs, financing and advice.
She presented the examples as evidence of how AI could address shortages in agricultural expertise and financial exclusion. The intervention took place during a high-level roundtable at the forum, held in Kigali from August 31 to September 4, 2026.
This could be the first step toward understanding dark matter as a particle.
Originally published by KT Press. Summarized and contextualized automatically by DistantNews, with a note on how the source frames the story. Not individually reviewed before publishing. How this works.