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Using Radio and Image Data for Surveillance

Joyce Nakatumba-Nabende of Makerere University in Uganda will use artificial intelligence to mine data from local village radio stations to generate timely data on crop pests and disease in sub-Saharan Africa. Crop loss due to pests and disease threatens the economic survival of smallholder farmers, and access to surveillance data is critically important yet often unaffordable. Local radio shows are a powerful source of information flow in rural African villages: they cover topics including politics, policy, climate, and social circumstances, in addition to crop concerns. Collectively, this information provides a holistic representation of current events in these communities. They will analyze local broadcasts to generate crop surveillance data that is linked to the local community situation. Radio content will be collected at low cost through a collaboration with Pulse Labs Kampala, and they will build artificial intelligence models based on deep neural networks and keyword identification to mine the data. The results will be combined with photographs of diseased crops provided by local farmers and used to train machine learning models to ultimately extract radio information in multiple languages and with diverse accents. This project will provide near real-time crop surveillance data and allow for timely responses to threats.

More information about Tools and Technologies for Broad-Scale Pest and Disease Surveillance of Crop Plants in Low-Income Countries (Round 22)

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