EAGER SitS: Bury and Forget Nitrogen Sensors Coupled With Remote Sensing for Soil Health

农田过度施肥导致氮径流，造成严重的饮用水污染以及商业渔业和旅游业的衰退。因此，拥有准确的预测氮土壤模型是至关重要的，它可以帮助农民通过准确地知道使用哪种类型的肥料以及准确地在田地的何时何地施用肥料来减少肥料的使用。然而，这些土壤模型的准确性是缺乏的，因为目前在田地内许多点获得的土壤氮浓度数据成本过高，技术上具有挑战性。这项研究将创造低成本的传感器，可以通过电力将土壤氮水平（铵离子和硝酸盐离子浓度水平）从不同的土壤深度和位置传输到一个中央集线器，这样数据就可以通过互联网传输并远程分析。可以安装低成本数据传输电子设备的传感器将由低成本的石墨烯（碳）制成，这种材料是一次性的，可以使用可扩展的制造协议来制造。完成后的传感器将在番茄植株周围的土壤中进行测试，以获得高分辨率的时空氮数据，以改进土壤氮模型，供农民使用。该项目的目标是开发“埋后即忘”的氮传感器，并结合遥感技术，实时分析土壤健康状况。该传感器将采用柔性石墨烯电极和离子载体膜功能化，用于使用激光刻字和喷墨打印技术检测土壤中的铵离子和硝酸盐离子（目标1）。这些传感器网络将使用商用蓝牙网状网络模块开发，用于传感器电源、计算和通信（目标2）。该项目将阐明传感器深度和广播频率，这些传感器深度和广播频率能够/需要使用桶式方法成功地监测土壤中氮。该传感器网络将与现有的作物模型合并，并使用试验台设施中的模型番茄系统在田间相关条件下进行挑战（目标3）。该试验台设施将用于从土壤中收集高分辨率氮传感器数据，同时监测植物的归一化差异植被指数，作为整合遥感和实时现场测量的基准。提议的项目将导致新的：1)无线氮传感器（不稳定和移动）；2)了解土壤氮与地上植物生理的时空动态关系；3)了解微/纳米传感器地下土壤数据的缩放，长时间的信号采集/管理，以及确定土壤中最大无线数据传输深度；4)将土壤传感器结果与当前的现场尺度工具（如遥感）相结合的最佳管理实践。该项目将首次将同一样品的原位纳米传感器、遥感和作物模型连接起来，从而建立一个平台，以提高对土壤生物地球化学、传感器网络和基本时空尺度原则的理解。该项目将促进快速研究，以改进经验模型参数（作物系数），并验证遥感（黄叶和养分胁迫之间的联系）和原位土壤传感器（养分命运和运输）中的假设。除了测试已开发的传感器系统外，该项目还将为土壤养分传感器的开发、测试和部署建立战略和最佳实践，这些传感器可以在任何地方进行传感器测试和/或假设测试，从而改进模型和观测网络，以管理土壤健康。这样的传感器网络和由此产生的模型有望实现精准农业，在精准农业中，肥料只在需要时以计量的方式喷洒到田地的特定位置。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。

Excess fertilizer application from farm fields results in nitrogen runoff which causes major drinking water contamination as well as commercial fishing and tourism industry decline. Therefore, it is vitally important to have accurate predictive nitrogen soil models that can help farmers reduce fertilizer use by knowing exactly what type of fertilizer to use and precisely when and where in a field to apply. However, the accuracy of these soil models is lacking because soil nitrogen concentration data acquired at numerous points within a field is currently cost prohibitive and technically challenging. This research will create low-cost sensors that can electrically transmit soil nitrogen levels (ammonium and nitrate ion concentration levels) from various soil depths and locations to a central hub so that data can be transmitted through the internet and analyzed remotely. Sensors that can be fitted with low-cost data transmission electronics will be made of low-cost graphene (carbon) that is disposable and can be created using scalable manufacturing protocols. The completed sensors will be tested in the soils surrounding tomato plants to acquire high resolution spatial and temporal nitrogen data for improving soil nitrogen models that can be utilized by farmers. The objective of this project is to develop bury-and-forget nitrogen sensors coupled with remote sensing technologies for real-time analysis of soil health. The sensors will be developed with flexible graphene electrodes functionalized with ionophore membranes for sensing of ammonium and nitrate ions in soils using laser inscribing and inkjet printing techniques (Aim 1). A network of these sensors will be developed using commercial Bluetooth-based mesh network modules for sensor power, computing, and communications (Aim 2). This project will elucidate the sensor depth and broadcast frequency that is capable/needed for successful in-soil nitrogen monitoring using a bucket brigade approach. This sensor network will be merged with existing crop models developed and challenged with in-field relevant conditions using a model tomato system in a testbed facility (Aim 3). The testbed facility will be used for collecting high resolution nitrogen sensor data from the soil coupled with monitoring of the Normalized Difference Vegetation Index of the plants as benchmarks to integrate remote sensing and real-time field measurements. The proposed project will lead to new: 1) wireless nitrogen sensors (both labile and mobile); 2) knowledge of spatiotemporal dynamics of soil nitrogen coupled with above ground plant physiology; 3) knowledge of scaling micro/nanosensor subsurface soil data, long-duration signal acquisition/curation, and pinpointing the maximum wireless data transmission depth in soil; and 4) best management practices for coupling soil sensor results to current field-scale tools such as remote sensing. The project will be the first to connect in-situ nanosensors, remote sensing, and crop modeling for the same sample, therein establishing a platform for improving understanding of soil biogeochemistry, sensor networks, and fundamental spatiotemporal scaling principles. This project will facilitate rapid studies for improving empirical model parameters (crop coefficients), as well as to validate assumptions in remote sensing (links between yellowing leaves and nutrient stress) and in-situ soil sensors (nutrient fate and transport). In addition to testing the developed sensor systems, this project will establish strategies and best practices for the development, testing, and deployment of soil nutrient sensors that can be reproduced anywhere for sensor testing and/or hypothesis testing, leading to improved models and observation networks to manage soil health. Such sensor networks and resultant models are expected to lead to precision agriculture where fertilizers are spread onto specific locations of the field in a metered fashion only when needed.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.