Crop management decision support systems are specialized tools for farmers
that reduce the riskiness of revenue streams, especially valuable for use under
the current climate changes that impact agricultural productivity.
Unfortunately, small farmers in India, who could greatly benefit from these
tools, do not have access to them. In this paper, we model an individual
greenhouse as a Markov Decision Process (MDP) and adapt Li and Li (2019)'s
Follow the Weighted Leader (FWL) online learning algorithm to offer crop
planning advice. We successfully produce utility-preserving cropping pattern
suggestions in simulations. When we compare against an offline planning
algorithm, we achieve the same cumulative revenue with greatly reduced runtime.

为了帮助小农户在气候变化下提高农业生产力，本研究将农作物管理决策支持系统模型化为一个马尔可夫决策过程，并采用 Li and Li (2019) 的 Follow the Weighted Leader (FWL) 在线学习算法为农户提供种植建议，通过模拟成功提出了保持效用的农作物种植模式，并与离线规划算法相比，大大减少了运行时间，但实现了相同的累计收益。