Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation
Keywords:Machine Learning, Agritech, Greenhouse Control, Constraint Programming
AbstractIncreasing 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.
How to Cite
van Bokkem, D., van den Hemel, M., Dumančić, S., & Yorke-Smith, N. (2023). Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15731-15737. https://doi.org/10.1609/aaai.v37i13.26867
IAAI Technical Track on emerging Applications of AI