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.