#agriculture

Predictive farming 

This Project will investigate artificial intelligence (AI) to improve weather forecasts and use crop models for making better farming decisions. The expected outcome is protocols for integrating AI-enhanced weather forecasts and crop models into farm management and planning tools. This is expected to result in farmers making more informed decisions that optimise resource use, boost crop yields, and enhance overall farm productivity and profitability while minimising environmental impact. 

Crop disease management

This project aims to integrate novel automated disease surveillance technology into real-time management of crop diseases for improved yield and sustainability outcomes. The expected outcome is grower recommendations for crop diseases informed by real-time automated disease surveillance. The potential benefit is improved productivity and sustainability through reduced crop losses and chemical inputs.

Optimising vegetation management and grazing in solar farms

This project aims to use high spatial, spectral and temporal resolution satellite images and artificial intelligence to derive measurements that enhance vegetation management and grazing efficiency in solar farms. The expected outcome is to develop algorithms that can be applied to near real-time satellite data to enable the spatially comprehensive measurements of vegetation growth, grazing patterns overgrazing, curing rating for decision making in monitoring and determining the effectiveness of land management strategies.