Unmanned aerial vehicles (UAV) with onboard sensors become a cost-effective way of crop remote sensing in largescale farms. However, current vision-based crop aerial sensing methods suffer from occlusion issue and require large amount of annotation data. In this paper, we target one particular crop species, corn, and propose to use 3D modeling and simulation to help resolve the issues. We first develop a corn-field 3D model and a crop aerial sensing (CAS) simulation framework. Then we use the CAS framework to generate synthetic data to train various deep learning models for corn leaf segmentation. In addition, we change the 3D model parameters in CAS, e.g., distances between individual corn plants, to derive leaf area index (LAI) correction coefficients for various corn plant and row spacings. Our experimental results from real-world UAV images show that our leaf segmentation model using synthetic data from the CAS framework outperforms state-of-the-art segmentation models by 1.4-3.3%. Our simulation results show that the plant and row spacings of a corn-field have significant effects on correcting the UAV image-based LAI, which can be underestimated by a factor of 2.6, due to the overlap and occlusion issues.

We present a containerized mobile sensing simulation (CMOS) framework developed for smart agriculture applications. This framework includes 1) 3D environment and object modeling, 2) mobile platform motion planning and control, and 3) optical sensing simulation, all implemented and connected within containers. Specifically, we build a user-friendly interface for 3D modeling, e.g., cornfield modeling using Blender. We use an unmanned aerial vehicle (UAV) as our mobile sensing platform and integrate UAV 3D model, flight path planning and control with robot operating system (ROS) packages and the Gazebo simulator. We also implemented optical sensing, e.g., collecting RGB image data from cameras in our simulation framework. This framework can be used not only in leaf area index correction and other analytical support for agriculture operations, but also as a synthetic data annotation tool for leaf segmentation and other smart agriculture applications. We demonstrate the major components of the CMOS framework, and how to use it to automatically annotate image data for the leaf segmentation application.