Catherine Nakalembe

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 the 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 underground 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 U.S.

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.