# An Online Optimization-Based Decision Support Tool for Small Farmers in India: Learning in Non-stationary Environments

arXiv: https://arxiv.org/abs/2311.17277 · HTML: https://mlsanity.com/paper/2311.17277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [CropGym: a Reinforcement Learning Environment for Crop Management](https://mlsanity.com/paper/2104.04326/) (2021)

## Built on by (most cited first, 1 of 1)

- [Efficient Restarts in Non-Stationary Model-Free Reinforcement Learning](https://mlsanity.com/paper/2510.11933/) (2025)

## Abstract (arXiv)

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.