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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