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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publicationAAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations
EditorsBrian Williams, Yiling Chen, Jennifer Neville
PublisherAmerican Association for Artificial Intelligence (AAAI)
Number of pages7
ISBN (Electronic)9781577358800
Publication statusPublished - 2023
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: 7 Feb 202314 Feb 2023

Publication series

NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023


Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/TerritoryUnited States

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