Modelling Greenhouse Microclimate in Agriculture Based on Deep Learning

Greenhouse microclimate forecasting is critical for monitoring crop growth and resource efficiency in controlled environment agriculture. However, the accuracy and reliability of greenhouse microclimate forecasting face significant challenges due to complex spatiotemporal interactions between environmental parameters and their nonlinear dependencies across multiple temporal and spatial scales. To address these challenges and enable robust method development, this thesis recorded diverse microclimate data from three greenhouses, which provided multi-regional, multi-seasonal, multi-temporal resolution and spatial granularity datasets. Supported by these datasets, this thesis developed three forecasting methods and frameworks that integrate deep learning with multi-scale temporal convolutions, physics-informed constraints, and spatiotemporal modelling techniques to improve accuracy and interpretability of greenhouse microclimate forecasting. These frameworks progressively advance from single-parameter temperature forecasting to multi-task learning (simultaneous forecasting of temperature, humidity and light intensity), from purely data-driven to physics-informed approaches, and from single-point forecasting to three-dimensional spatiotemporal modelling.