AI in agriculture: hybrid machine learning models for nitrogen simulation

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Agricultural simulation models are used to guide nitrogen management to reduce nitrogen loss and its environmental impact, but they were developed using constrained datasets, which restricts them to site- or regional-specific simulations. This project adopts a novel approach to addressing these problems by applying machine learning-based data analytics. The project will refine the linkages between nitrogen losses and their key drivers, and improve the existing agroecosystem models through data imputation, parameter optimisation and module enhancement. The outcomes of this project will lead to an accurate prediction of nitrogen losses from agriculture, advancement in agroecosystem models and their adaptability to a global context.