Forecasting Black Sigatoka Infection Risks with Latent Neural ODEs (Papers Track)

Yuchen Wang (University of Toronto); Matthieu Chan Chee (University of Toronto); Ziyad Edher (University of Toronto); Minh Duc Hoang (University of Toronto); Shion Fujimori (University of Toronto); Jesse Bettencourt (University of Toronto)

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Time-series Analysis Agriculture & Food


Black Sigatoka is the most widely-distributed and destructive disease affecting banana plants. Due to the heavy financial burden of managing this infectious disease, farmers in developing countries face significant banana crop losses. The spread of black Sigatoka is highly dependent on weather conditions and though scientists have produced mathematical models of infectious diseases, adapting these models to incorporate climate effects is difficult. We present MR. NODE (Multiple predictoR Neural ODE), a neural network that models the dynamics of black Sigatoka infection learnt directly from data via Neural Ordinary Differential Equations. Our method encodes external predictor factors into the latent space in addition to the variable that we infer, and it can also predict the infection risk at an arbitrary point in time. Empirically, we demonstrate on historical climate data that our method has superior generalization performance on time points up to one month in the future and unseen irregularities. We believe that our method can be a useful tool to control the spread of black Sigatoka.

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