Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge. Acquiring high-quality 3D MS point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure. Here, we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field (NeREF) for radiometric calibration. This approach was used to acquire 3DMPCs of perilla, tomato, and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness (EWT) estimation. The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6% compared with the fixed viewpoints alone. The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error (RMSE) of 58.93% for extracted reflectance spectra. The RMSE for chlorophyll content and EWT predictions decreased by 21.25% and 14.13% using partial least squares regression with the generated 3DMPCs. Collectively, our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions, which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits, and thus will facilitate plant biology and genetic studies as well as crop breeding.

中文翻译：

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一种基于无人地面车辆表型分析的生成三维多光谱点云的方法，用于破译植物性状的空间异质性


融合三维 （3D） 和多光谱 （MS） 成像数据有望实现高通量和全面的植物表型分析，以破译基因组到表型组的知识。由于具有复杂冠层结构的植物的 3D 数据质量差且辐射校准方法有限，因此获取植物的高质量 3D MS 点云 （3DMPC） 仍然具有挑战性。在这里，我们提出了一种新的 3D 空间-光谱数据融合方法，通过集成用于自适应数据采集的次优视图规划和用于辐射校准的神经参考场 （NeREF） 来收集植物的高质量 3DMPC。该方法用于获取紫苏、番茄和油菜籽植物的 3DMPCs，这些植物具有不同的植物结构和叶片形态特征，通过叶绿素含量和等效水厚度 （EWT） 估计的准确性进行评估。结果表明，与单独使用固定视点相比，该方法采集的植物点云的完整性平均提高了 23.6%。使用半球参考的基于 NeREF 的辐射定标优于传统定标方法，将提取的反射光谱的均方根误差 （RMSE） 降低了 58.93%。使用生成的 3DMPC 的偏最小二乘回归，叶绿素含量和 EWT 预测的 RMSE 分别降低了 21.25% 和 14.13%。总的来说，我们的研究提供了一种在自然光条件下收集植物高质量 3DMPCs 的有效方法，从而提高了植物形态和生理性状表型的准确性和全面性，从而促进了植物生物学和遗传研究以及作物育种。