Abstract:
To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds.
摘要:
为了快速获得水稻植株表型性状,本研究提出了六种水稻表型特征的计算过程(例如,冠部直径,茎的周长,植物高度,表面积,volume,和投影叶面积)使用地面激光扫描(TLS)数据,并提出了水稻植株分耕数的提取方法。具体来说,第一次,我们设计并开发了一种基于PyQt5框架和Open3D库的三层体系结构的水稻植株自动表型提取工具。结果表明,测量值与提取值之间的线性确定系数(R2)在所选的四个验证特征中具有更好的可靠性。冠径均方根误差(RMSE),茎的周长,植物高度稳定在厘米水平,耕种者的数量低至1.63。冠径的相对均方根误差(RRMSE),植物高度,分till数保持在10%以内,茎周长为18.29%。此外,用户友好的自动提取工具可以有效地提取水稻植株的表型特征,为快速获取水稻植株点云的表型性状特征提供了方便的工具。然而,更多水稻植物样本数据支持的表型特征提取结果的比较和验证,以及精度算法的改进,仍然是我们未来研究的重点。该研究可为利用三维点云提取作物表型提供参考。