Machine learning meets African agriculture
In 2016, a crop-destroying caterpillar, Fall Armyworm (FAW) was first detected in Africa. The crop pest has since devastated agriculture by infecting millions of corn fields, which threatens food security on the continent. Farmers who rely on harvests for food need to combat the pest, which has now spread to India and China.
That’s where Nazirini Siraji comes in. She is one of several developers working to provide farmers with new tools to fight FAW. After codelabs hosted by a Google developer group in Mbale, Uganda, she created the “Farmers Companion App” using TensorFlow, Google’s open-source machine learning platform. It’s a free app that identifies when a crop has FAW and which stage the worm is in its lifecycle (and therefore how threatening it is and how far it is likely to spread). It also advises on which pesticides or treatments are best to stop the worm spreading any further. The app is already working in the field, helping farmers around Mbale to identify FAW.
They continue to improve the app so it can identify more pests and diseases. Nazirini shows the impact that developers can have on agricultural issues like FAW and across other sectors, too. We visited Nazirini and her team this year, here’s more about their story:
Learn more about how others are using TensorFlow to solve all kinds of problems.
