Crop health assessment and yield prediction from multi-spectral remote sensing imagery are ongoing areas of interest in precision agriculture. It is in these contexts that simulation-based techniques are useful to investigate system parameters, perform preliminary experiments, etc., because remote sensing systems can be prohibitively expensive to design, deploy, and operate. However, such techniques require realistic and reliable models of the real world. We thus present a randomizable time-dependent model of corn (Zea mays L.) canopy, which is suitable for procedural generation of high-fidelity virtual corn fields at any time in the vegetative growth phase, with application to simulated remote sensing of agricultural scenes. This model unifies a physiological description of corn growth subject to environmental factors with a parametric description of corn canopy geometry, and prioritizes computational efficiency in the context of ray tracing for light transport simulation. We provide a reference implementation in C++, which includes a software plug-in for the 5th edition of the Digital Imaging and Remote Sensing Image Generation tool (DIRSIG5), in order to make simulation of agricultural scenes more readily accessible. For validation, we use our DIRSIG5 plug-in to simulate multi-spectral images of virtual corn plots that correspond to those of a United States Department of Agriculture (USDA) site at the Beltsville Agricultural Research Center (BARC), where reference data were collected in the summer of 2018. We show in particular that 1) the canopy geometry as a function of time is in agreement with field measurements, and 2) the radiance predicted by a DIRSIG5 simulation of the virtual corn plots is in agreement with radiance-calibrated imagery collected by a drone-mounted MicaSense RedEdge imaging system. We lastly remark that DIRSIG5 is able to simulate imagery directly as digital counts provided detailed knowledge of the detector array, e.g., quantum efficiency, read noise, and well capacity. That being the case, it is feasible to investigate the parameter space of a remote sensing system via “end-to-end” simulation.

Library of Congress Subject Headings

Plant health--Remote sensing; Corn--Growth--Computer simulation; Multispectral imaging--Computer simulation

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


Jan van Aardt

Advisor/Committee Member

John Kerekes

Advisor/Committee Member

Scott Brown


RIT – Main Campus

Plan Codes