Reinforcement learning for crop management support: Review, prospects and challenges

Gautron Romain, Maillard Odalric-Ambrym, Preux Philippe, Corbeels Marc, Sabbadin Régis. 2022. Reinforcement learning for crop management support: Review, prospects and challenges. Computers and Electronics in Agriculture, 200:107182, 14 p.

https://doi.org/10.1016/j.compag.2022.107182

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Résumé : Reinforcement learning (RL), including multi-armed bandits, is a branch of machine learning that deals with the problem of sequential decision-making in uncertain and unknown environments through learning by practice. While best known for being the core of the artificial intelligence (AI) world's best Go game player, RL has a vast range of potential applications. RL may help to address some of the criticisms leveled against crop management decision support systems (DSS): it is an interactive, geared towards action, contextual tool to evaluate series of crop operations faced with uncertainties. A review of RL use for crop management DSS reveals a limited number of contributions. We profile key prospects for a human-centered, real-world, interactive RL-based system to face tomorrow's agricultural decisions, and theoretical and ongoing practical challenges that may explain its current low uptake. We argue that a joint research effort from the RL and agronomy communities is necessary to explore RL's full potential.

Mots-clés Agrovoc : intelligence artificielle, apprentissage machine, aide à la décision, incertitude statistique, analyse du risque, système d'aide à la décision

Mots-clés complémentaires : Algorithme

Mots-clés libres : Reinforcement Learning, Decision support systems, Artifical intelligence, Risk awareness, Bandit algorithms, Uncertainty

Classification Agris : U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 7 (2019-) - Hors champs stratégiques

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