More than 8,000 satellites orbit Earth, taking photos every day. Food security specialist and TED Fellow Catherine Nakalembe shows how she uses this imagery to help smallholder farmers across Africa prepare for floods, droughts and crop failures. Learn why real innovation isn’t always about shinier technology — it’s about making the tech truly fit the problem it’s solving. (Recorded at TED Fellows Films 2025 on April 7, 2025)
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If farming was your primary source of income, and if you can't grow anything, there's not much you can do. It's very demoralizing. It's very overwhelming. A lot of the countries where I work, farmers face fires, pests, diseases, droughts and floods. If crops fail, no food is available for a lot of people who depend on what they produce. It translates into, you know, sometimes an entire generation being undermined. My name is Catherine Nakalembe and I'm a satellite food security specialist. I use satellite data to map and monitor crops, and then work to make sure that information can be used by decision makers and organizations that support farmers. I primarily focus on Africa: Uganda, Kenya, Tanzania, Zambia, Mali, Senegal. Last time I checked, there were over 8,000 satellites observing our Earth. They take pictures every day. You can use those images to map what crops are growing where, how much it's going to rain, where weather system might be coming from, where it might be impacted and how badly it might be impacted. I can just sit on my computer and tell you anywhere in the world, rainfall, drought, floods, name it. I can tell you where it is. We have tons of information, tons of data about what's happening. We're living in a fantastic age. We have huge advances with AI. We're able to process a lot more data and information. The problem is, when you look deeply at any place, you start to see problems with a lot of the existing products. They're not tailored to the on-the-ground contexts. Sometimes it's simply that the data is just wrong. A lot of the models are trained very well to predict for European or US agriculture. In Europe, most of the farms have like, a single crop. They're really big and it's very easy to model. In Kenya, in Uganda, in Rwanda, however, the fields are so tiny, they have so many different crops in them, and farmers do things so differently. It's like a tapestry. With those images fields are misrepresented. So there are places where there are no crops but are labeled as crops. There are places where there should be crops and people that are completely missing. When you go and try to assess something for the ministry and you use that as input, you're basically feeding them garbage. You want to make sure that what you're feeding in is really good, and that actually requires work. To train a model to understand that complexity, you need a lot of examples. You have to go on the ground. What we did is we use GoPros. You wear a GoPro as you're driving on a motorcycle, or you can do it in the car. And as you drive we take pictures. Basically Google Street View, not for streets but for crops. So the camera is actually facing towards the field and then adapt basically what would be face detection but instead of detecting faces, cats, dogs, etc., we modified it to detect maize, beans, cassava. We covered all of western Kenya in two weeks with just two teams, collected over five million images. A lot of them with volunteers every day, mototaxi drivers, students. This would allow us to build a more complex model that can learn from all these different examples, from all the different contexts. There was a flood in Kenya in 2024. It happened really, really rapidly. And, you know, the entire country was affected, pretty much. I got an email from the Ministry of Agriculture asking to do an assessment, using satellite data to look at where floods happened, where were crops, and give an estimate of what the total area of cropland that's been affected was. And then what the ministry does with the information is they make their response programs, which is where do we need to go to provide seeds so people can replant. It's an example of an action that was taken. It's really powerful to be able to do that. True innovation is not about high-tech systems, but about making the technology fit the problem. We have to provide really good information to the people who can do something with it. If we do this correctly, we can save time, we can save money, we can save livelihoods.
