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

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.