DOI: 10.3390/agronomy14010011 ISSN: 2073-4395

A Decision-Making System for Cotton Irrigation Based on Reinforcement Learning Strategy

Yi Chen, Zhuo Yu, Zhenxiang Han, Weihong Sun, Liang He
  • Agronomy and Crop Science

This article addresses the challenges of water scarcity and climate change faced by cotton cultivation in the Xinjiang region of China. In response, a precise irrigation model based on reinforcement learning and the crop model DSSAT is proposed. The experimental site chosen for this study is Changji City in northwest China’s Xinjiang province. Integrating the cotton model, CSM-CROPGRO, from the DSSAT model with reinforcement learning algorithms, a decision system was developed to provide accurate irrigation strategies that maximize cotton yield. The experimental results demonstrated that our approach significantly improved cotton yield and, compared to genetic algorithms, reduced water consumption while increasing production. This provides a better solution for developing cotton cultivation in the Xinjiang region. Additionally, we analyzed the differences in irrigation strategies among different decision scenarios, and the results showed that the reinforcement learning method achieved higher yields in water application trends during different periods. This research offers new ideas and methods for improving cotton-crop-management decisions. The study’s focus on maximizing cotton yield while reducing water usage aligns with the sustainable management of water resources and the need for agricultural adaptation to changing climate conditions. It highlights the potential of reinforcement learning methods in improving irrigation decision-making and their applicability in addressing water scarcity challenges. This research contributes to the advancement of cotton crop management and provides valuable insights for agricultural decision-makers in the Xinjiang region and beyond.

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