Reinforcement learning for crop management support: Review, prospects and challenges
2022
Gautron, Romain | Maillard, Odalric-Ambryn | Preux, Philippe | Corbeels, Marc | Sabbadin, Régis | Agroécologie et intensification durables des cultures annuelles (UPR AIDA) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Cali] ; Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Rome] (Alliance) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL) ; Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) | International Institute of Tropical Agriculture (IITA Kenya) ; International Institute of Tropical Agriculture [Nigeria] (IITA) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | This work has been supported by: The French Agricultural Research Centre for International Development (CIRAD). The Consultative Group for International Agricultural Research (CGIAR) Platform for Big Data in Agriculture. Special thanks to Brian King. The French Ministry of Higher Education and Research, Hauts-de-France region, Inria within the Scool team project and MEL.
International audience
Mostrar más [+] Menos [-]Inglés. Highlights: • Reinforcement learning is a promising AI framework to support crop management. • Reinforcement learning-based crop management support literature is scarce. • A reinforcement learning-based system should learn from interactions on the ground. • Crop management support is related to many reinforcement learning research questions. • Joint research by the reinforcement learning and agronomy communities is required.Abstract: 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.
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Información bibliográfica
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