This theme focusses on the development of intelligent algorithms that will automate greenhouse control based on the biological relevant processes/characteristics collected with sensors and analysed with climate-crop models (theme 1 and 2). Current greenhouse control aims to provide optimal climate conditions for plant growth and development while raising resource use efficiency. In modern high-tech greenhouses it is growers’ task to define the climate, irrigation and crop management strategies. This is done with the aid of a climate process computer, sensor information, long-term accumulated experience and intuition.
The greenhouse systems present a high degree of complex and interactive processes of physical, chemical and biological nature which makes it challenging to control for human intelligence. Large potential lays in the field of Artificial Intelligence to maintain and even improve the control of highly complex systems with many interacting factors. Paired with the lack of sufficiently qualified personnel in greenhouse horticulture, artificial intelligence becomes increasingly interesting for protected cultivation. In this theme, we will develop robust and scalable automated control algorithms able to generate decisions for both climate and crop in the view of autonomous greenhouse production systems which are also profitable and sustainable. We will select intelligent algorithms, train the selection in a virtual environment based on climate and crop models and validate resulting algorithms in greenhouse trials.