SBIR Phase I: Cosmic Ray Neutron Sensing for Soil Water Measurements at Irrigation Decision Resolution

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业影响是一种非接触式、作物不可知、自动、始终开启、不漂移、具有成本效益的方法，可以根据每个灌区的具体数据测量大片农田的土壤湿度。其目标是使缺水环境中的灌溉者能够在现有水资源的情况下最大限度地提高产量，并通过使灌溉者能够根据植物的精确需求校准其系统来减轻干旱和含水层减少的影响。该系统的影响是最大限度地提高灌溉用水的生产率，减少作物种植者和其他灌溉者的浪费和成本。这种产品的商业化将提供精确灌溉所需的数据，以满足日常地下水需求，广泛减少农业和其他市场的用水量。该技术将研究人员和学者使用的已知土壤湿度监测方法转化为一种工具，为管理世界水资源的最终用户提供可操作的数据。拟议的研究和开发的目标是验证来自一组较小的宇宙射线中子感测（CRNS）系统的阵列数据，这些数据被输入到插值算法中，可以提供具有每个灌溉点水平分辨率的土壤湿度热图，用于田间水分测量。该项目将消除与将CRNS方法应用于子场测量尺度相关的主要技术风险，通过颗粒湿度分析提供可操作的数据。该数据将与使用人工探测系统进行的多小时高强度人工分析产生的类似热图进行比较。这种比较旨在支持自动化，精确的灌溉系统，以尽量减少水的使用和纠正过度浇水。将解决的主要技术障碍包括：从一系列较小尺寸的CRNS系统收集数据，这些CRNS系统以所需的测量分辨率间隔放置在现场；在大时间尺度上整合中子计数数据，这是克服低效率探测器相关噪声所必需的；以及提供与灌溉管理人员相关的必要的时变信息的能力。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。

The broader impact/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is a touchless, crop agnostic, automatic, always on, non-drifting, and cost-effective way of measuring soil moisture in large fields with data specific to each irrigation zone. The goal is to enable irrigators in water-scarce environments to maximize yields given the water available, and to mitigate the impacts of drought and aquifer reduction by enabling irrigators to calibrate their systems to the precise needs of the plants. The impact of this system is to maximize productivity of irrigation water, reducing waste and costs for crop growers and other irrigators. The commercialization of this product will provide the data needed for precision irrigation to respond to daily ground water needs, broadly reducing water consumption in agriculture and other markets. This technology translates a known methodology for soil moisture monitoring used by researchers and academics into a tool that delivers actionable data to end users that steward the world’s water resources. The goal of the proposed research and development is to verify that data from an arrayed set of smaller Cosmic Ray Neutron Sensing (CRNS) systems, ingested into an interpolation algorithm, can provide a soil moisture heat map with resolution at the level of each irrigation point for water measurements in the field. This project will remove the main technical risks associated with applying the CRNS methodology to a sub-field measurement scale, providing actionable data through granular moisture analysis. This data will be compared with a similar heat map generated by a multi hour, high labor assay using a manual probing system. This comparison seeks to support automated, precision irrigation systems to minimize water usage and correct for overwatering. The main technical hurdles that will be addressed include: the data collection from an array of smaller-form-factor CRNS systems that are placed on the field at the spacing of the desired measurement resolution; integration of neutron count data over the large temporal scales that are necessary to overcome the noise associated with the less efficient detectors; and the ability to provide the necessary temporally-changing information that is relevant to the irrigation manager.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.