Collaborative Research: CPS: TTP Option: Medium: Sharing Farm Intelligence via Edge Computing

在数据共享时代，科学家分享从农业数据收集和数据处理中吸取的经验教训仍然具有挑战性，不安全且耗时。该项目的重点是通过交叉植物科学，安全网络系统，软件工程和地理空间科学的专业知识来缓解这些挑战。拟议中的网络物理系统将在实验室进行评估，并部署在密苏里州、伊利诺伊州和田纳西州的真实的农场上。所有成果将与旨在提高粮食安全和改善人类健康和营养的国际组织共享。拟议中的系统将安全地协调使用传感器收集的数据，如高光谱和热成像相机，以收集大豆，高粱和其他作物的图像。然后，预处理的植物数据集将通过基于Web的系统以不同格式提供给科学家和农民，准备由深度学习算法处理或由瘦客户端使用。从不同农场收集的数据将用于训练分布式深度学习系统，使用优化隐私和训练时间的新型架构。这种机器学习系统将用于预测植物胁迫和检测病原体。最后，网络物理系统将把新的数据处理软件与现有的NSF资助的硬件平台集成在一起，在边缘计算中引入新的算法贡献，并向农民提供反馈，从而闭合循环。 该项目的成果将影响高价值作物的研究，这些作物具有很高的自动化水平，例如保护性农业和沙漠农业中的鱼类作物水培系统。 计划开展的外联活动将影响到国际干旱地区农业研究中心（旱地农研中心）合作者支持的针对小农户的解决方案。 虽然这项工作将侧重于为农业应用启用数据科学，但这项工作也将为其他物联网应用的管理提供信息，例如，该奖项反映了NSF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。

In the era of data sharing, it is still challenging, insecure, and time-consuming for scientists to share lessons learned from agricultural data collection and data processing. The focus of this project is to mitigate such challenges by intersecting expertise in plant science, secure networked systems, software engineering, and geospatial science. The proposed cyber-physical system will be evaluated in the laboratory and deployed on real crop farms in Missouri, Illinois, and Tennessee. All results will be shared with international organizations whose goal is to increase food security and improve human health and nutrition.The proposed system will securely orchestrate data gathered using sensors, such as hyperspectral and thermal cameras to collect imagery on soybean, sorghum, and other crops. Preprocessed plant datasets will be then offered to scientists and farmers in different formats via a web-based system, ready to be processed by deep learning algorithms or consumed by thin clients. Data collected from different crop farms will be used to train distributed deep learning systems, using novel architectures that optimize privacy and training time. Such machine learning systems will be used to predict plant stress and detect pathogens. Finally, the cyber-physical system will integrate novel data processing software with existing NSF-funded hardware platforms, introducing novel algorithmic contributions in edge computing and giving feedback to farmers, closing the loop. The results of this project will impact research on high-value crops with significant levels of automation, such as those in protected agriculture and fish crop hydroponics systems in desert farming. Planned outreach activities will impact solutions for smallholder farmers that collaborators at the International Center for Agricultural Research in the Dry Areas (ICARDA) support. Although this work will focus on enabling data science for farming applications, the work will also inform management of other IoT applications, e.g., smart and connected healthcare or other cyber-human systems.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.