Lawns, also known as turf, cover an estimated 128,000 km 2 in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain. With this consumption comes a large amount of environmental, economic, and social incentive to make turf irrigation systems as efficient as possible. Recent work introduced the concept of distributed control in irrigation systems, but existing control strategies either do not take advantage of the distributed control, or do not revise the strategy over time in response to collected data. In this work, we introduce OPTICS, a data-driven control strategy that self-improves over time, adapts to the local specific conditions and weather changes, and requires virtually no human input in both setup and maintenance providing a plug-and-play system that requires minimal pre-deployment efforts. In addition to substantial improvements in ease-of-use, we find across 4 weeks of large-scale irrigation system deployment that OPTICS improves system efficiency by 12.0% in comparison to industry best and 3.3% in comparison to academic state of the art. Despite using less water, OPTICS also was found to improve quality of service by a factor of 4.0× compared to industry best and 2.5× compared to academic state of the art.

中文翻译：

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光学

草坪，也称为草皮，占地约 128,000 公里2仅在北美，景观需求就占住宅领域淡水消耗量的 30%。这种消耗带来了大量的环境、经济和社会激励，以使草坪灌溉系统尽可能高效。最近的工作在灌溉系统中引入了分布式控制的概念，但现有的控制策略要么没有利用分布式控制，要么没有随着时间的推移修改策略以响应收集的数据。在这项工作中，我们介绍了 OPTICS，这是一种数据驱动的控制策略，可以随着时间的推移自我改进，适应当地的特定条件和天气变化，并且在设置和维护过程中几乎不需要人工输入，从而提供即插即用需要最少的部署前工作的系统。除了易用性的显着改进外，我们发现在 4 周的大规模灌溉系统部署中，OPTICS 将系统效率提高了 12.0%，与行业最佳水平相比，与学术水平相比提高了 3.3%。尽管使用的水更少，但 OPTICS 还被发现将服务质量提高了 4.0 倍，与行业最佳水平相比，与学术水平相比提高 2.5 倍。