A Machine Learning Pipeline to Predict Vegetation Health (Papers Track) Spotlight

Thomas Lees (University of Oxford); Gabriel Tseng (Okra Solar); Simon Dadson (University of Oxford); Alex Hernández (University of Osnabrück); Clement G. Atzberger (University of Natural Resources and Life Sciences); Steven Reece (University of Oxford)

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Agricultural droughts can exacerbate poverty and lead to famine. Timely distribution of disaster relief funds is essential to help minimise the impact of drought. Indices of vegetation health are indicative of higher risk of agricultural drought, but their prediction remains challenging, particularly in Africa. Here, we present an open-source machine learning pipeline for climate-related data. Specifically, we train and analyse a recurrent model to predict pixel-wise vegetation health in Kenya.

Recorded Talk (direct link)