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Actionable Subgroup Discovery and Urban Farm Optimization

Alexandre Millot
Romain Mathonat
Rémy Cazabet
Jean-François Boulicaut


Designing, selling and/or exploiting connected vertical urban farms is now receiving a lot of attention. In such farms, plants grow in controlled environments according to recipes that specify the different growth stages and instructions concerning many parameters (e.g., temperature , humidity, CO2, light). During the whole process, automated systems collect measures of such parameters and, at the end, we can get some global indicator about the used recipe, e.g., its yield. Looking for innovative ideas to optimize recipes, we investigate the use of a new optimal subgroup discovery method from purely numerical data. It concerns here the computation of subsets of recipes whose labels (e.g., the yield) show an interesting distribution according to a quality measure. When considering optimization, e.g., maximizing the yield, our virtuous circle optimization framework iteratively improves recipes by sampling the discovered optimal subgroup description subspace. We provide our preliminary results about the added-value of this framework thanks to a plant growth simulator that enables inexpensive experiments.
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hal-02483314 , version 1 (18-02-2020)



Alexandre Millot, Romain Mathonat, Rémy Cazabet, Jean-François Boulicaut. Actionable Subgroup Discovery and Urban Farm Optimization. International Symposium on Intelligent Data Analysis (IDA), Apr 2020, Konstanz, Germany. pp.339-351, ⟨10.1007/978-3-030-44584-3_27⟩. ⟨hal-02483314⟩
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