MIRO MIRANDA LORENZ

"DOMAIN-INFORMED NEURAL NETWORKS FOR EARTH OBSERVATION"

Yield prediction is an essential task in the agricultural sector. Yet, it is still challenging due to multidimensional factors defining the yield, including environmental factors, management, the genotype, and their complex interactions. Considering changing and fluctuating climate conditions, reliable yield predictions are increasingly challenging. Here, machine learning plays an increasingly important role. Still, we observe weak performances and overconfident models. This colloquium provides an insight of how yield prediction can be addressed using stochastic and domain-informed models, and highlights current challenges and future research directions.

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