Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation
Dirk van Bokkem, Max van den Hemel, Sebastijan Dumančić, Neil Yorke-Smith
Abstract
Increasing global food demand, accompanied by the limited number of expert growers, brings the need for more sustainable and efficient horticulture. The controlled environment of greenhouses enable data collection and precise control. For optimally controlling the greenhouse climate, a grower not only looks at crop production, but rather aims at maximising the profit. However this is a complex, long term optimisation task. In this paper, Constraint Programming (CP) is applied to task of optimal greenhouse economic control, by leveraging a learned greenhouse climate model through a CP embedding. In collaboration with an industrial partner, we demonstrate how to model the greenhouse climate with an LSTM model, embed this LSTM into a CP optimisation framework, and optimise the expected profit of the grower. This data-to-decision pipeline is being integrated into a decision support system for multiple greenhouses in the Netherlands.
BibTeX
@article{van Bokkem_van den Hemel_Dumančić_Yorke-Smith_2024, title={Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26867}, DOI={10.1609/aaai.v37i13.26867}, abstractNote={Increasing global food demand, accompanied by the limited number of expert growers, brings the need for more sustainable and efficient horticulture. The controlled environment of greenhouses enable data collection and precise control. For optimally controlling the greenhouse climate, a grower not only looks at crop production, but rather aims at maximising the profit. However this is a complex, long term optimisation task. In this paper, Constraint Programming (CP) is applied to task of optimal greenhouse economic control, by leveraging a learned greenhouse climate model through a CP embedding. In collaboration with an industrial partner, we demonstrate how to model the greenhouse climate with an LSTM model, embed this LSTM into a CP optimisation framework, and optimise the expected profit of the grower. This data-to-decision pipeline is being integrated into a decision support system for multiple greenhouses in the Netherlands.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={van Bokkem, Dirk and van den Hemel, Max and Dumančić, Sebastijan and Yorke-Smith, Neil}, year={2024}, month={Jul.}, pages={15731-15737} }