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

Abstract

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

Plant phenotyping relevance

UAV画像由来の3D点群からソルガム個体穂を検出・特徴付け、長さ・幅・体積・重量関連形質を推定する手法の開発と検証が中心である。

Code and data availability

The paper describes UAV-derived 3D point cloud phenotyping of sorghum panicles, but the Data Availability Statement explicitly states no data sharing, and no public dataset, image, code, or model repository is mentioned anywhere in the supplied blocks.

No evidence-backed public reproduction asset is currently recorded.