Article Dans Une Revue Computers and Electronics in Agriculture Année : 2022

Reinforcement Learning for crop management

Résumé

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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Dates et versions

hal-03834290 , version 1 (29-10-2022)

Identifiants

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Romain Gautron, Odalric-Ambrym Maillard, Philippe Preux, Marc Corbeels, Régis Sabbadin. Reinforcement Learning for crop management. Computers and Electronics in Agriculture, 2022, 200, pp.107182. ⟨10.1016/j.compag.2022.107182⟩. ⟨hal-03834290⟩
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