Machine Learning aided Crop Yield Optimization

TL;DR

This paper introduces a crop simulation environment integrated with reinforcement learning algorithms to optimize crop yield while reducing resource usage, addressing global food security challenges.

Contribution

It presents a novel hybrid plant modeling and data-driven approach using DRL for crop yield optimization with an OpenAI Gym interface.

Findings

DRL algorithms can discover new crop management policies

The approach reduces water and fertilizer usage

Potential to meet global food demands

Abstract

We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield. We empirically show that DRL algorithms may be useful in discovering new policies and approaches to help optimize crop yield, while simultaneously minimizing constraining factors such as water and fertilizer usage. We propose that this hybrid plant modeling and data-driven approach for discovering new strategies to optimize crop yield may help address upcoming global food demands due to population expansion and climate change.