Non-destructive measurement of crop growth stage, canopy development, and height may be useful for more efficient crop management practices. In this study, ultrasonic sensing technology was investigated as one approach for corn plant canopy characterization. Ultrasonic echo signals from corn plant canopies were collected using a lab-based sensor platform. Echo signal peak features were extracted from multiple scans of plant canopies. These features included peak amplitude, scan number, and time of flight. Feature vectors with similarities were clustered together to identify individual leaves of the canopy. The mean height of the clustered data of individual leaves was estimated. The growth stage of each plant was estimated based on the number of leaves detected. Regression analysis was used to describe the relationship between manually measured leaf heights and ultrasonic estimates. A leaf-signal interaction model was developed to predict which parts of leaf surfaces will result in echo signals detectable by the sensor. The aim of this research was to develop a sensing system which extracted information from an ultrasonic sensor that could be used for a variety of sensing operations in precision agriculture and to better understand the relationship between corn plant canopy and ultrasonic signals.