Download PDFOpen PDF in browserAnalyzing Reinforcement Learning Algorithms for Nitrogen Fertilizer Management in Simulated Crop Growth12 pages•Published: July 18, 2022AbstractEstablishing 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.Keyphrases: crop growth, fertilizer management, precision agriculture, reinforcement learning In: Aurona Gerber (editor). Proceedings of 43rd Conference of the South African Institute of Computer Scientists and Information Technologists, vol 85, pages 146-157.
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