Identification, monitoring and control of ground water balance in agriculture and forestry

Controlled water management allows agriculture and forestry to efficiently use water resources and adapt to changing weather conditions. The motivations for controlling the groundwater level are many, ranging from ensuring favorable growth of plants to scheduling of farming operations, efficient temporal and spatial distribution of water resources, and reduction of greenhouse gas emissions and water loadings. The practical needs in cultivation and forestry stem from the increased size of farms and plantations, expansion in the amount of responsibilities on farmers, and needs for a holistic, timely and anticipating control and maintenance of all the resources under various uncertainties, such as weather, soil properties and and crop growth. Model-based controlled drainage and irrigation provide means for management of water balance, whether via automatic control including reduction of the operator work load, or by supporting human decision making including proper monitoring of the water balance status.

This position focuses on short term dynamic modeling, model-based monitoring and automatic control of the water balance in agriculture and forestry. In particular, the possibilities for automated in-situ modeling and model-tuning for control purposes are examined, taking into account the realistic conditions at agricultural and forestry sites. The goal is in making the modelling process straightforward and automated, so as to cover feasibly the main dynamics of various fields differing in geometry, soil, crop, etc. Based on a feasible water balance, control problem formulations, and practical tests, the related monitoring and automatic control problems will be solved and demonstrated.

The ideal candidate for this PhD project will possess a comprehensive academic background in control engineering and modelling, with a particular interest in the IoT-technology and agriculture. Furthermore, good knowledge on process identification, monitoring, control, and related software tools like MATLAB, Python and R is greatly appreciated. Familiarity with the subsurface drainage and ground water balances is a bonus. The selected candidate will be working in multidisciplinary academic work community, so good team working skills and desire to learn new things across disciplinary boundaries are essential.

Luke

Intelligent machines and systems research unit, University of Oulu

Enso Ikonen

Toni Liedes

Maarit Liimatainen (Luke)

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