3D Characterization of Sorghum Panicles Using a 3D Point Cloud Derived from UAV Imagery

Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles needed assessing genetic diversity, variety selection, yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed detecting characterizing derived from UAV images. The RGB ratio was used to filter non-panicle points out select potential panicle points. Individual were detected concept tree identification. Panicle length width determined We proposed cylinder fitting disk stacking estimate volumes, which are directly related yield. results showed that correlation coefficient average between UAV-based ground measurements 0.61 0.83, respectively. UAV-derived diameter more highly correlated weight than measurements. yielded R2 values 0.77 0.67 actual weight, experimental can provide reliable consistent parameters, weight.