Intelligent irrigation management using machine learning, sensors, and crop models

In response to growing water scarcity and food demands worldwide, there is a need for agriculture to increase production while minimizing pressure on limited freshwater resources. In this context, the overall aim of this project is to evaluate and develop novel machine-learning and artificial intelligence techniques to support the next-generation of real-time irrigation decision support tools. Three key research questions will be explored through the project: (1) What are the gains for farmers (water use reductions, improved crop yields, higher profits) from adoption of adaptive real-time irrigation control systems relative to existing rule-based irrigation scheduling? (2) Which types of observation or forecast data are most useful for efficient real-time optimization of irrigation decision-making? (3) How can machine learning and artificial intelligence be used to support scalable application and uptake of these technologies and methods in real-world farming systems?