Analyzing Reinforcement Learning Algorithms for Nitrogen Fertilizer Management in Simulated Crop Growth

Michael Vogt, Benjamin Rosman
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引用次数: 1

Abstract

Establishing intelligent crop management techniques for preserving the soil, while providing next-generational food supply for an increasing population is critical. Nitrogen fertilizer is used in current farming practice as a way of encouraging crop development; however, its excessive use is found to have disastrous and long-lasting effects on the environment. This can be reduced through the optimization of fertilizer application strategies. In this work, we apply a set of reinforcement learning algorithms – the DQN, Double DQN, Dueling DDQN, and PPO – to learn novel strategies for reducing this application in a simulated crop growth setting. We provide an analysis of each agent’s ability and show that the Dueling DDQN agent can learn favourable strategies for minimizing nitrogen fertilizer application amounts, while maintaining a sufficient yield comparable to standard farming practice.
模拟作物生长过程中氮肥管理的强化学习算法分析
建立智能作物管理技术来保护土壤,同时为不断增长的人口提供下一代食物供应是至关重要的。在目前的农业实践中,氮肥被用作促进作物生长的一种方式;然而,人们发现它的过度使用对环境产生了灾难性和持久的影响。这可以通过优化施肥策略来减少。在这项工作中,我们应用了一组强化学习算法- DQN, Double DQN, Dueling DDQN和PPO -来学习在模拟作物生长环境中减少这种应用的新策略。我们对每种药剂的能力进行了分析,并表明Dueling DDQN药剂可以学习到减少氮肥施用量的有利策略,同时保持与标准农业实践相当的足够产量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.60
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0.00%
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