Elements: A Deep Neural Network-based Drone (UAS) Sensing System for 3D Crop Structure Assessment

该项目开发了一种3D重建传感系统，可安装在无人机系统（UAS）上，供农业研究人员、种植者和服务提供商用于评估作物生长情况。将人工智能（AI）技术应用于大规模农业重建应用，该传感系统将能够以比目前依赖于光探测和测距（LiDAR）的标准低得多的成本估算大覆盖区域的作物结构。该项目将开发和完善基于深度神经网络的3D评估工作流程，仅基于低成本和轻型2D激光雷达和彩色相机配置。研究人员、种植者和服务提供商将能够根据作物生长的3D时间序列提取出详细的作物结构并预测产量。该技术将为目前的3D激光雷达传感器方法提供一种更便宜的替代方案，并且该传感系统还可以应用于相关领域，如高通量表型和一般陆地植被的变异估计。包括外联和推广活动，以便向包括农业研究人员、种植者和服务提供者在内的利益攸关方提供研究成果。博士生、本科生和高中生将通过该项目接受培训，其中包括通过罗切斯特理工学院成像科学高中暑期实习计划培训高中生的暑期活动。该奖项由先进网络基础设施办公室颁发，由美国国家科学基金会生物科学理事会生物基础设施部和美国国家科学基金会计算机与信息科学与工程理事会信息与智能系统部联合支持。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。

This project develops a 3D reconstruction sensing system that can be installed on unmanned aerial systems (UAS), to be used by agricultural researchers, growers, and service providers to assess crop growth. Applying Artificial Intelligence (AI) technology for large scale agriculture reconstruction applications, the sensing system would be able to estimate crop structure for a large coverage area at a much lower cost than current standards that rely on light detection and ranging (LiDAR). The project would develop and refine a deep neural network-based 3D assessment workflow, based solely on a low cost and lightweight 2D LiDAR and color camera configuration. Researchers, growers, and service providers would be able to extract detailed crop structure and forecast yields, based on a 3D time series of crop growth. The technology would provide a less expensive alternative to the current 3D LiDAR sensor approach, and the sensing system could also be applied to related areas such as high-throughput phenotyping and variation estimation of general terrestrial vegetation. Outreach and extension activities are included, to deliver research outcomes to the stakeholders, including agricultural researchers, growers and service providers. PhD students, undergraduates, and high school students will be trained through this project, including a summer activity training high school students through the Rochester Institute of Technology Imaging Science High School Summer Intern Program.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Biological Infrastructure within the NSF Biosciences Directorate, and by the Division of Information and Intelligent Systems within the NSF Computer and Information Science and Engineering Directorate.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.