A greenhouse production system is complex, many different factors influence vegetable growth (light, temperature, humidity, CO2, water, nutrients etc.) and resource usage (energy, water etc.), while pest and diseases can inhibit crop growth. The latter can be influenced by various operational and strategic crop management decisions (crop density, pruning and harvest strategies etc.).
A grower has to decide on the right setpoints for all parameters at every moment. Interaction of multiple states, control of multiple actuators towards desired control (ventilation, heating, cooling, dehumidification, fogging, shading, artificial lighting, crop management actions etc.), scouting for pests and diseases and release of predators or precision spraying are among the decisions to be made. A well-educated and very experienced grower can oversee most aspects of the system. However, such growers are scarce worldwide. In order to feed a growing number of people in the future with a vitamin rich diet, new and fully automated production systems need to be developed that can be operated by less experienced growers and also by non-agricultural investors.
Fully automated remote control
We assume that artificial intelligence, well-chosen machine learning and computer vision algorithms can optimize such a complex greenhouse crop production system. Novel insights in plant physiology from the interaction of all the growing parameters, innovations in sensing technology and intelligent interpretation of sensor signals can boost productivity while minimising resource use at the same time. The third challenge has one target: must be a fully automated remote control.
This project is an important step towards computer operated greenhouses to better feed the world with healthy products and make optimum use of inputs/resources like energy and water.