SBIR Phase II: Machine Learning Driven Synthetic Sensor for Plant Water Stress

这个SBIR第二阶段项目将推进一种新的植物胁迫传感、预测和解决系统，为永久和特种作物种植者提供负担得起的指导。这一指导方针将同时减少灌溉用水量和农用化学品使用量，同时提高作物产量和农民利润。该项目将开发易于使用的设备和软件，提供与已安装的灌溉系统和程序兼容的指导。这项技术在美国的潜在市场为30亿美元，在国际上为300亿美元。该系统能够减少灌溉用水和化学品的使用，具有深远的社会效益。该系统将保护稀缺的淡水资源，允许人口增长和灌溉土地的增加。该系统将减少农业活动造成的水污染，解决美国每年2100亿美元的问题；此外，它还将提高农业产量，确保不断增长的人口有足够的食物，并减少农药和杀菌剂的使用。这个SBIR第二阶段项目将展示一个软件和分布式硬件系统，用于测量和预测减产植物非生物和生物压力源，并向农民提供干预指导，以缓解这些压力源。为了在局部水平上预测植物胁迫，必须融合和分析许多数据馈送，以创建一个估计植物水分胁迫、预测小气候条件和执行局部植物病虫害模拟的综合传感器。该项目将推进超低功耗传感器阵列、广域网络、机器学习和大数据解释的人机界面设计等领域。该项目将通过增加小气候和病虫害模型预测算法来扩展测试系统，以精确地进行灌溉、化肥、杀菌剂、农药和生物刺激剂应用的时间和位置定向干预，以缓解植物压力。该奖项反映了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。

This SBIR Phase II project will advance a novel system for sensing, predicting, and addressing plant stress, offering affordable guidance for permanent and specialty crop farmers. This guidance will simultaneously reduce irrigation water and agricultural chemical applications, while also increasing crop yields and farmer profits. The project will develop easy-to-use equipment and software that delivers guidance compatible with installed irrigation systems and processes. The addressable market for this technology is $3 B in the United States and $30 B internationally. The system's ability to reduce the use of irrigation water and chemicals has a profound societal benefit. The system will conserve scarce fresh water resources, allowing for population growth and an increase in irrigated land. The system will reduce water pollution from agricultural activities, addressing a $210 B annual problem in the United States; furthermore, it will improve agricultural yields, ensuring adequate food for growing populations, with reduced use of pesticides and fungicides.This SBIR Phase II project will demonstrate a software and distributed hardware system for measuring and predicting yield-reducing plant abiotic and biotic stressors, and delivering intervention guidance to farmers to mitigate these stressors. In order to predict plant stresses at a localized level, many data feeds must be fused and analyzed to create a synthetic sensor estimating plant water stress, predicting microclimatic conditions, and performing localized plant disease and pest modeling. The project will advance the fields of ultra-low power sensor arrays, wide-area networking, machine learning, and human interface design for big data interpretation. This project will expand a test system by adding microclimate and disease and pest model prediction algorithms for precision temporal and location-targeted interventions of irrigation, fertilizer, fungicide, pesticide, and biostimulant applications to mitigate plant stress.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.