Modern high-tech greenhouses are equipped with active control of actuators (for example heating, lighting and screening) to create the desired greenhouse climate for a crop. These intensive cultivation systems require a high use of natural resources such as energy, water and nutrients. In current practice, growers determine the set points for their greenhouse climate based on the crop status and their experience on how crop growth is affected by the climate. Actuators are operated to control the greenhouse based on these setpoints and sensors provide information on the realized climate. Automated greenhouse climate control algorithms were already developed decades ago. Nowadays, advanced crop growth models are also used to generate advise on the climate set-points.
The aim of this project is to further integrate climate and crop growth models and to link them to the sensors and actuators of the greenhouse (work package 2). This will form the basis to control the greenhouse climate based on self-learning algorithms that receive their information from crop and climate sensors (work package 3). This requires further knowledge about the physiological reactions of the crop to changing environmental conditions (work package 1). Read more about the workpackages here