Intelligent irrigation management using machine learning, sensors, and crop models
Lead Research Organisation:
University of Manchester
Department Name: Mechanical Aerospace and Civil Eng
Abstract
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?
Organisations
People |
ORCID iD |
Julien Harou (Primary Supervisor) | |
Thomas Kelly (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
NE/R009732/1 | 30/09/2017 | 29/09/2022 | |||
2222925 | Studentship | NE/R009732/1 | 30/09/2018 | 29/06/2022 | Thomas Kelly |