Sorghum panicle extraction from unmanned aerial system data

Attributes of sorghum panicle is a very important to assess overall crop condition, irrigation, and estimation of terminal yield. In this study, a novel method to extract sorghum panicle and estimate panicle volume of grain sorghum using an Unmanned Aerial System (UAS) is proposed. UAS data were acquired with 85% overlap at an altitude of 10m above ground. Ortho-mosaic image, Digital Surface Model (DSM), and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the images. Ground Control Points (GCP) were used for accurate geo-referencing. Sorghum panicles were determined from RGB image and DSM by using color ratio and circle fitting. Panicle volume was estimated by the cylinder fitting method and the disk stacking method. The results of this study showed that UAS data can provide non-destructive, more efficient, and may be considered to replace the field work.