SBIR Phase II: Low-cost in-planta nitrate sensor

这个小企业创新研究（SBIR）二期项目的更广泛影响/商业潜力是为农民提供一种低成本的植物传感器，用于直接、即时测量作物汁液中的硝酸盐氮(N)水平。该传感器的广泛采用可以支持具有成本效益和改进的氮肥管理，这可能提高农民的生产力和盈利能力。与目前的土壤氮水平测试方法（例如，收集土壤样本，将其运送到实验室，然后等待约1周进行实验室分析）相比，低成本、即时的植物秸秆硝酸盐测试读数将提供更多可操作的信息，以指导农民的施肥决策。通过更容易获得和更可操作的数据，肥料管理决策的改进有可能使美国每年减少约200万吨氮肥的总施用量。氮肥施用的大幅减少将减少农业的能源足迹，减少一氧化二氮（一种比二氧化碳强300倍的温室气体）的排放，并通过减少氮径流改善水质，通过改善农村水质和减少缺氧死亡区改善生态系统服务和人类健康。本项目代表了植物硝酸盐传感器在功能和应用方面的创新。该项目的目标是：i)改进传感器，以便在现场测量中可靠地部署；ii)在农民的田间进行研究，开发预测模型，根据玉米秸秆的传感器测量和其他数据输入硝酸盐水平，输出氮肥建议；iii)建立一个基于实验室的多探针硝酸盐传感器，将工作扩展到现有植物和土壤测试市场上的其他作物。预期的结果是，来自传感器的数据将用于建立预测模型，该模型将输出优于传统模型的最佳氮肥建议。这一目标的实现将导致坚固的低成本传感器的商业化，提供快速测量植物汁液硝酸盐。这种能力将使基于数据驱动的氮需求预测模型为农民提供低成本的肥料建议成为可能。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 potential of this Small Business Innovation Research (SBIR) Phase II project is to provide farmers with a low-cost plant sensor for direct, instantaneous measurement of nitrate-nitrogen (N) levels in crop sap. Widespread adoption of the sensor could support cost-effective and improved N fertilizer management, which may increase farmers’ productivity and profitability. Low-cost, instantaneous nitrate testing readouts from plant stalks will provide more actionable information to guide farmers’ fertilization decisions than current methods to test N levels in soil (e.g., collect soil samples, ship them to lab, and then wait ~1 week for lab analysis). The improvements in fertilizer-management decisions, enabled by more accessible and more actionable data, have the potential to reduce total N fertilizer applications in the US by some 2 million tons annually. This large reduction in N fertilizer applications would decrease the energy footprint of agriculture, reduce emissions of nitrous oxide, a greenhouse gas 300 times as potent as carbon dioxide, and improve water quality through reductions in N runoff, improving ecosystem services and human health through improved rural water quality and the reduction in hypoxic dead zones. The proposed project represents an innovation in the function and application of an in planta nitrate sensor. The project goals are to: i) improve the sensor for reliable deployment in field measurements; ii) conduct research in farmers’ fields to develop predictive models that input nitrate levels from sensor measurements of corn stalks and other data to output N fertilization recommendations; and iii) build a lab-based multi-probe nitrate sensor that extends the work to other crops in the existing plant and soil testing market. Anticipated results are that the data from the sensors will be used to build predictive models that output optimum N fertilization recommendations that will outperform conventional models. The accomplishment of this goal will lead to the commercialization of rugged low-cost sensors that provide rapid measurements of plant sap nitrate. This ability will make it possible to provide farmers with low-cost fertilizer recommendations based on data-driven, predictive modeling of N demand.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.