Ground-Based Robotic Sensing of an Agricultural Sub-Canopy Environment

Airborne remote sensing is a useful method for measuring agricultural crop parameters over large areas; however, the approach becomes limited to above-canopy characterization as a crop matures due to reduced visual access of the sub-canopy environment. During the growth cycle of an agricultural crop, such as soybeans, the micrometeorology of the sub-canopy environment can significantly impact pod development and reduced yields may result. Larger-scale environmental conditions aside, the physical structure and configuration of the sub-canopy matrix will logically influence local climate conditions for a single plant; understanding the state and development of the sub-canopy could inform crop models and improve best practices but there are currently no low-cost methods to quantify the sub-canopy environment at a high spatial and temporal resolution over an entire growth cycle. This work describes the modification of a small tactical and semi-autonomous, ground-based robotic platform with sensors capable of mapping the physical structure of an agricultural row crop sub-canopy; a soybean crop is used as a case study. Point cloud data representing the sub-canopy structure are stored in LAS format and can be used for modeling and visualization in standard GIS software packages.