Zoek medewerkers/organisaties dr.ir. S van Mourik
Naamdr.ir. S van Mourik

OmschrijvingAssistant professor
OrganisatieDepartement Plantenwetenschappen
OrganisatieeenheidAgrarische Bedrijfstechnologie
Telefoon+31 317 481 275
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Telefoon 2
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BezoekadresDroevendaalsesteeg 1
PostadresPostbus 16
Reguliere werkdagen
Ma Di Wo Do Vr


Precision Farming allows us to manage farms sustainably, animal friendly, and labour efficient. This is achieved by precise dosage, timing, and allocation of inputs like energy, fertilizer, pesticides, fresh water, and antibiotics. The key challenge to create the intelligence required to manage complex processes under uncertain, and variable circumstances.

Our Precision Farming team develops strategies for creating intelligence in the form of prognosis, diagnosis, and control. Key methods from Systems and Control are mathematical modelling of physical and physiological system processes, feedback control, model predictive control, adaptive control, state filtering, and systems identification. Key methodologies to deal with uncertainty, and variation are data analysis, machine learning, experimental calibration and validation.




I am involved in the following research projects:

LED it be 50%  (led by Leo Marcelis, Horticulture and Product Physiology)
The aim is a 50% energy reduction in greenhouses via smart LED light management. We investigate possible ways to control LED light and other climate factors in a cost effective manner. http://www.stw.nl/nl/content/p13-20-save-led-it-be-50

Energy saving in greenhouse crop production by flexible management
The goal of this project is to develop greenhouse climate management support that substantially saves on gas, by employing data streams on weather and on the energy grid. This type of management will at the same time reduce the peak loads on the electricity grid, thereby helping the transition to cleaner energy. https://www.nwo.nl/onderzoek-en-resultaten/onderzoeksprojecten/i/50/30550.html

Sensing and modelling morphological traits and social interactions to identify vulnerable cows in dairy herds Together with Lely Industries, we focus on the following objectives: 1) to sense and model morphological traits and social interactions in dairy cows, 2) to identify vulnerable cows based on the dynamics of model outputs to improve dairy herd management.

Veerkracht 2 Together with Wageningen Livestock Research we focus on improving animal welfare by predicting the resilience of cows during vulnerable periods. We do this by integrating experiment and statistical testing, in order to investigate which signal, or combination of signals, are key for predicting vulnerability.

Low-cost low-risk irrigation under uncertain agricultural circumstances Various types of unknown or unpredictable variation, limit the manageability of farming processes, making them inefficient and costly. This causes risk avoiding behaviour of farmers, e.g., in the form of excessive over application of water, fertilizer, pesticide, and antibiotics, with drastic consequences for our environment. As a case study, we investigate how to optimize irrigation under uncertainty with respect to water stress in lettuce. We consider various way to optimize; from irrigation scheduling to hardware innovation.

Modelling uncertainty in controlled environmental agricultural systems Inaccurate climate sensing in greenhouses are associated with considerable energy loss due to mismanagement. Increasing the number of sensors maybe costly and impractical. We investigated the added value of diagnosing the climate state via filtering sensor data using a climate model and a noise model.





The following courses are aimed at students that conduct research of biosystems under variable and uncertain circumstances.   

  1. Data analysis (FTE-26306) for BSc students, aimed at finding and analysing data driven evidence. The course is inspired by the Advanced Statistics courses, and tailored to biosystems engineering applications. The underlying model framework is a general linear model, built solely upon data.
  2. Machine learning (FTE-35306) for MSc students. This course is an introduction to automated learning in order to make diagnosis and predictions. The underlying modeling framework is consist of various nonlinear structures, but all are black-box (not based on underlying principles).
  3. Precision Farming (FTE-35806) for MSc students. In this course we build upon classic control engineering theory, and design ways to create methods for precise dosage, timing, and allocation of inputs. In particular, the focus is on prediction, diagnosis, and control. The model framework consists of nonlinear differential equations.
  4. Statistical uncertainty analysis of dynamic models: PhD tutorial. Introduction to uncertainty propagation in dynamic input-state-output models. The model framework is general, to allow nonlinearity, and first principle models. The latter is important for investigating the underlying mechanisms of a system, management design, and systems design. For more info, see https://www.pe-rc.nl/postgraduate-courses/uncertainty-analysis-dynamic-models


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