PFI-TT: Prototyping an Adaptive Irrigation System for Saline Water

PFI-TT：盐水自适应灌溉系统原型设计

• 批准号: 1827665
• 负责人: Junko Munakata Marr
• 金额: $ 20万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827665&HistoricalAwards=false
• 关键词: PFI; TT; Prototyping; Adaptive; Irrigation

The broader impact/commercial potential of this PFI project lies in improved understanding of development of smart systems and in potentially significant resource savings. Irrigation with saline water is a complex problem requiring consideration of many factors such as irrigation water quality, soil conditions, plant tolerance thresholds, and weather forecasts. This project will develop a prototype wireless sensor network and a control system that consider sensor inputs, user-defined thresholds and weather forecasts to determine when and where to irrigate. From an economic perspective, implementation of even a single soil moisture sensor in home lawns can significantly reduce water consumption and produce a return on investment within a few months. Cities stand to save millions of dollars by implementing efficient irrigation technology to reduce water use. The proposed adaptive system for irrigation with reclaimed water thus represents a tremendous potential market for more efficient water use.The proposed project offers a transformative approach for landscape irrigation based on closed-loop control. Landscape irrigation accounts for a majority of urban summer domestic water use; irrigating with reclaimed water has the potential to substantially extend water resources but may pose hazards to environmental and plant health. Existing ?smart? irrigation control systems have proved water-efficient but have limitations and have not been widely adopted. The proposed system is unique in its combination of wireless water and soil quality sensors into an advanced network capable of controlling several critical agronomic variables, while welcoming but limiting the need for human inputs. A team of domain experts in academia and industry will work together to understand the practical challenges and to design such an adaptive irrigation system to address widespread water resource issues. Key contributions of the proposed research are (1) realizing a truly self-organizing ad-hoc wireless irrigation sensor and actuator network that integrates traditional monitoring and stakeholder input for adaptive irrigation control; and (2) testing the efficient and protective delivery of saline irrigation water using the network. Such fundamental and applied knowledge will provide the basis to ultimately realize significant water savings while protecting human and plant health.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该PFI项目更广泛的影响/商业潜力在于提高对智能系统开发的理解，并潜在地节省大量资源。盐水灌溉是一个复杂的问题，需要考虑许多因素，如灌溉水质、土壤条件、植物耐受阈值和天气预报。该项目将开发一个原型无线传感器网络和一个控制系统，该系统考虑传感器输入、用户定义的阈值和天气预报，以确定何时何地灌溉。从经济角度来看，即使在家庭草坪上安装一个土壤湿度传感器，也可以显着减少用水量，并在几个月内产生投资回报。通过实施有效的灌溉技术来减少用水，城市将节省数百万美元。因此，拟议的再生水灌溉适应性系统代表了更有效用水的巨大潜在市场。该项目为景观灌溉提供了一种基于闭环控制的变革性方法。景观灌溉占城市夏季生活用水的大部分；用再生水灌溉有可能大大扩大水资源，但可能对环境和植物健康造成危害。现有智能?灌溉控制系统已证明是节水的，但有局限性，没有得到广泛采用。该系统的独特之处在于，它将无线水和土壤质量传感器结合成一个先进的网络，能够控制几个关键的农艺变量，同时欢迎但限制了对人工输入的需求。一个由学术界和工业界专家组成的团队将共同努力，了解实际的挑战，并设计这样一个适应性灌溉系统，以解决广泛的水资源问题。本研究的主要贡献有：(1)实现了一个真正自组织的无线灌溉传感器和执行器网络，该网络集成了传统监测和利益相关者的输入，用于自适应灌溉控制；(2)试验了盐渍灌溉水利用网络的高效、保护性输送。这些基础知识和应用知识将为最终实现显著节水，同时保护人类和植物健康提供基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。