However, these systems assess greenhouse processes in a different way and thus lead to different recommendations for greenhouse control, depending on the intended objective. In addition, the implicit knowledge of the operator is usually not taken into account in such systems.
Deployment of Artificial Intelligence
It is a challenge to develop an Aritificial Intelligence (AI) system that assesses different processes for controlling greenhouses and includes the operator’s experience. The operator shares his decisions and reasoning with the AI system and also learns from the AI system. In time, the quality of the advice improves: the system becomes more autonomous and partly takes over the control of the greenhouse.
AI is applied to make the best choices for greenhouse control, such as indoor climate, energy and water consumption, CO2 production and crop growth. However, sometimes conflicting goals need to be reached. For example: temperature and relative humidity in greenhouses must be within a certain range, energy and net water consumption must be as low as possible, crop development must be as fast as possible whereas the quality needs to be acceptable.
To this end, the Greenhouse AI Accumulator (GAIA) is being developed; it allows various underlying existing subcontrollers to work together to achieve a good balance through a collective control strategy. Therefore, the system must receive input from sensors in the greenhouse, including data on crop development.
The use of existing and new AI technologies, such as Model-based Predictive Control and/or Reinforcement Learning, will be examined to determine whether they can provide the correct advice. In addition, advice will always be accompanied by a good explanation through explainable AI techniques. The operator can learn from the decisions of the system and will also be enabled to give feedback with substantiation about the correctness of an advice. The feedback will used by the AI system to create improved future advice. In this way, the system learns from the operator, who makes decisions based on ‘green fingers’ founded on years of experience: ‘co-learning’.
What challenge does it solve?
The aim of the project is to support the greenhouse operator in making the correct decisions to achieve maximum yield and product quality in greenhouses at minimum cost and impact on the environment. In the longer term, this should lead to largely autonomously functioning greenhouses, making food production in greenhouses easier and more feasible in large parts of the world.
What does the use case deliver?
This AI development helps the Dutch greenhouse horticulture sector to stay ahead in the development of high-tech greenhouses. The application of AI systems is especially important for high-tech greenhouses at locations in the world with less agronomic expertise. In those places, in-depth expertise will more often be located at a distance (‘remote control’), thefore it is is required that one can rely more on autonomous systems.
The intended results are:
- A proof-of-concept of a first version of GAIA in which AI techniques are applied and fine-tuned for optimal greenhouse control;
- A mechanism for explainability and a hybrid-AI approach for co-learning between operator/grower and GAIA;
- Experimental results of the GAIA proof-of-concept for a specific greenhouse to test the usability for the operator/grower;
This project is part of the Appl.AI programme of TNO and is partly funded by the start impulse the NL AIC received from the Dutch government for research and development of AI applications. GAIA is developed by TNO in cooperation with Ridder and Hortivation (through the adjacent project AGROS, co-funded by TKI “Tuinbouw en Uitgangsmaterialen”).