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

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.

Automated summary

Reinforcement learning is a branch of machine learning that deals with sequential decision-making in uncertain environments. It has the potential to address some of the criticisms of crop management decision support systems, but its application in this field is currently limited. Further research and collaboration between the reinforcement learning and agronomy communities are needed to fully explore its potential in agricultural decision-making.