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

农田肥料过量使用导致氮素径流，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，拥有准确的氮素土壤预测模型至关重要，该模型可以通过准确地知道要使用什么类型的肥料以及准确地在田间何时何地施肥来帮助农民减少化肥的使用。然而，这些土壤模型的精确度不足，因为在田地内的许多点获得的土壤氮浓度数据目前成本高昂，技术上具有挑战性。这项研究将创造低成本的传感器，可以将土壤氮素水平(铵离子和硝酸根离子浓度水平)从不同的土壤深度和地点电子传输到中央枢纽，以便通过互联网传输数据并进行远程分析。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，这种石墨烯是一次性的，可以使用可扩展的制造协议来制造。完成的传感器将在番茄植株周围的土壤中进行测试，以获得高分辨率的空间和时间氮素数据，以改进农民可以利用的土壤氮素模型。该项目的目标是开发与遥感技术相结合的一次性氮传感器，用于实时分析土壤健康。将利用柔性石墨烯电极和离子载体膜开发传感器，利用激光刻字和喷墨打印技术检测土壤中的铵离子和硝酸根离子(目标1)。将使用基于蓝牙的商用网状网络模块开发这些传感器的网络，用于传感器供电、计算和通信(目标2)。该项目将阐明能够/需要使用斗式方法成功监测土壤中氮素的传感器深度和广播频率。该传感器网络将与现有作物模型合并，这些模型是利用试验台设施中的番茄模型系统开发的，并在田间相关条件下受到挑战(目标3)。试验台设施将用于从土壤中收集高分辨率的氮传感器数据，并监测植物的归一化差异植被指数，以此作为基准，将遥感和实时实地测量结合起来。拟议的项目将产生新的成果：1)无线氮素传感器(包括不稳定的和可移动的)；2)土壤氮素的时空动态知识，结合地上植物生理学；3)衡量微/纳米传感器地下土壤数据的知识，长时间的信号采集/管理，并精确确定土壤中的最大无线数据传输深度；以及4)将土壤传感器结果与目前的实地尺度工具，如遥感相结合的最佳管理做法。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，为改善对土壤生物地球化学、传感器网络和基本时空尺度原理的理解建立一个平台。该项目将促进改进经验模型参数(作物系数)的快速研究，并验证遥感(变黄的叶子与营养胁迫之间的联系)和现场土壤传感器(养分去向和迁移)中的假设。除了测试开发的传感器系统外，该项目还将制定开发、测试和部署土壤养分传感器的战略和最佳做法，这些传感器可以在任何地方复制，用于传感器测试和/或假设测试，从而改进管理土壤健康的模型和观测网络。这样的传感器网络和由此产生的模型预计将导致精准农业，只有在需要时才以计量的方式将化肥撒到田地的特定位置。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.