Excellence in Research: Developing a Robust, Distributed, and Automated Sensing and Control System for Smart Agriculture

为了适应快速增长的粮食需求，提高农业生产的质量和数量，有必要改进农业管理做法和农业领域的技术发展。该项目将协同控制、机器人、遥感和农业工程方面的专业知识，开发智能农业系统自动化监测的新方法，作为一种重要的网络物理系统（cps）。该合同支持基础研究，通过采用分布式机载网络传感器系统，为无人驾驶飞行器（uav）团队调查农场，开发智能农业系统的创新技术。与使用地面操作人员或车辆监测农场的传统作物管理方法不同，拟议的对农田进行空中监测的方法最大限度地减少了地面操作的部署，避免了对农场健康部分的作物造成损害。该项目的目标是(1)开发一种分布式机载监测系统，用于调查农场，以检测作物受损或营养缺乏的可能区域，以及(2)实施和演示开发的方法，用于协调无人机在北卡罗莱纳州农业和技术州立大学（NC A&amp；T）农场的农业应用。为了实现这些目标，将开发一种优化规划技术，用于协调无人机，同时结合其物理限制和安全考虑，确保对农场进行完整调查并尊重传感要求。将开发一种自动化决策技术来处理收集到的图像信息，并检测农场中的问题点及其大小和严重程度。与拟议的研究相结合的是一项创新的教育和推广计划，该计划将吸引各种各样的学生、农民和当地社区成员参与与stem相关的活动和该项目的农业技术方面，以提高公众对使用新技术进一步发展精准农业的认识。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。

To accommodate rapidly growing food demands and increase the quality and quantity of agricultural production, it is necessary to improve farming management practices and technological developments in agricultural fields. This project will synergize expertise in Control, Robotics, Remote Sensing, and Agricultural Engineering to develop new approaches for automated monitoring of smart agricultural systems as an important class of cyber-physical systems (CPSs). This award supports fundamental research to develop innovative techniques for smart agricultural systems by employing a distributed airborne networked sensor system for a team of Unmanned Aerial Vehicles (UAVs) to survey a farm. Unlike traditional crop management methods that use ground operators or vehicles for monitoring farms, the proposed approach for airborne monitoring of agricultural fields minimizes deployment of on-the-ground operations, avoiding damaging crops on healthy parts of the farms.The objectives of this project are (1) developing a distributed airborne monitoring system for surveying farms to detect possible zones of crop damage or nutrition deficiency, and (2) implementing and demonstrating the developed methods for coordination of UAVs for agricultural applications over the North Carolina Agricultural and Technical State University (NC A&T) farm. To achieve these objectives, an optimal planning technique will be developed for coordinating UAVs while incorporating their physical constraints and safety considerations, ensuring a complete survey of the farm and respecting sensing requirements. An automated decision-making technique will be developed to process the collected imagery information and detect the problem spots in the farm and their size and severity. Integrated with the proposed research is an innovative education and outreach plan that will engage a diverse range of students, farmers, and local community members in STEM-relevant activities and agritechnological aspects of this project to increase public awareness on using new technologies for further development of precision agriculture.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.