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通过动态剪切和负激励机制驱动的温室蔬菜灌的强化学习控制方法
Ruipeng Tang1, Jianxun Tang2, Mohamad Sofian Abu Talip1
1Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Frontiers in plant science
|November 24, 2025
概括
本研究介绍了用于智能温室灌的增强负激励近接政策优化 (ENPPO) 算法. 通过优化灌计划,ENPPO提高了用水效率和作物产量,优于传统方法.
科学领域:
- 农业工程 农业工程
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 温室蔬菜生产面临着传统灌的挑战,导致水的浪费和效率低下.
- 动态的环境条件和复杂的管理因素需要先进的灌控制.
研究的目的:
- 开发和评估一种先进的灌控制方法,用于可持续的温室蔬菜生产.
- 通过利用增强强化学习来解决传统灌的局限性.
主要方法:
- 引入增强的负激励近距离政策优化 (ENPPO) 算法.
- 集成动态剪切功能和对连续行动空间的负面激励.
- 利用实时传感器数据和历史记录进行最佳灌预测.
主要成果:
- 与PPO和TRPO相比,ENPPO显示出更高的预测准确度,融合效率和水资源利用率.
- 尽量减少过度和不足灌的情况.
- 促进了蔬菜产量和质量的提高,同时降低了生产成本.
结论:
- 恩波算法为温室智能灌管理提供了一种多功能技术解决方案.
- 这种方法具有巨大的潜力,可以通过优化资源利用来推进可持续农业实践.
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