Multi-objective optimization for large-scale allocation of soybean crops

Chen Mathilde, Makowski David, Tonda Alberto. 2024. Multi-objective optimization for large-scale allocation of soybean crops. In : GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference. Li Xiaodong (ed.), Handl Julia (ed.). SIVECO, ACM. New-York : ACM, 1174-1182. ISBN 979-8-4007-0494-9 GECCO '24: Genetic and Evolutionary Computation Conference, Melbourne, Australie, 14 Juillet 2024/18 Juillet 2024.

https://doi.org/10.1145/3638529.3654026

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Résumé : The optimal allocation of crops to different parcels of land is a problem of paramount practical importance, not only to improve production, but also to address the challenges posed by climate change. However, this optimization problem is inherently complex, characterized by a vast search space that renders traditional optimization techniques impractical without oversimplified assumptions. Compounding this challenge, climate change introduces conflicting objectives, as solutions aiming to just maximize total yield may be more susceptible to extreme weather events, and thus obtain more unpredictable year-by-year outcomes. In order to tackle this complex optimization problem, we propose a multi-objective approach, simultaneously maximizing the overall yield, minimizing the year-on-year yield variance, and minimizing the total cultivated surface. The approach exploits an established multi-objective evolutionary algorithm, and employs a machine learning model able to predict yield from weather and soil conditions, trained on historical data, making it possible to tackle allocation problems of large size. An experimental evaluation focusing on the allocation of soybean crops in the European continent for the years 2000-2023 shows that the proposed methodology is able to identify different trade-offs between the conflicting objectives, that an expert analysis later reveals to be realistic and meaningful for driving stakeholder decisions.

Mots-clés libres : Crop allocation, Crop yiel forecasting, Machine Learning, Multi-objective optimization

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) CLAND : Changement climatique et usage des terres

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