一种基于无人地面飞行器表型的方法,用于生成三维多光谱点云,以破译植物性状的空间异质性。

IF 17.1 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Molecular Plant Pub Date : 2024-10-07 Epub Date: 2024-09-14 DOI:10.1016/j.molp.2024.09.004
Pengyao Xie, Zhihong Ma, Ruiming Du, Xin Yang, Yu Jiang, Haiyan Cen
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引用次数: 0

摘要

融合三维(3D)和多光谱(MS)成像数据有望实现高通量、全面的植物表型分析,从而破译基因组到表型组的知识。由于三维数据质量较差,且针对具有复杂冠层结构的植物的辐射校准方法有限,因此获取高质量的植物三维多光谱点云(3DMPCs)仍具有挑战性。我们提出了一种新颖的三维空间-光谱数据融合方法,通过整合用于自适应数据采集的下一个最佳视角(NBV)规划和用于辐射校准的神经参考场(NeREF),采集高质量的植物三维多谱点云。我们采用这种方法采集了紫苏、番茄和油菜植物的三维多普勒成像图,这些植物具有不同的植物结构和叶片形态特征,并通过叶绿素含量和等效水厚度(EWT)估算的准确性进行了评估。结果表明,与单独使用固定视点相比,我们的方法收集的植物点云的完整性平均提高了 23.6%。基于 NeREF 的半球参考辐射校准优于传统校准方法,提取的反射光谱的均方根误差(RMSE)降低了 58.93%。利用生成的 3DMPCs 进行偏最小二乘回归(PLSR),叶绿素含量和 EWT 预测的均方根误差分别减少了 21.25% 和 14.13%。我们的研究提供了一种在自然光条件下采集高质量植物三维多普勒光谱的有效方法,提高了植物形态和生理性状表型的准确性和全面性,有助于植物生物学、遗传学研究和育种计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits.

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.

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来源期刊
Molecular Plant
Molecular Plant 植物科学-生化与分子生物学
CiteScore
37.60
自引率
2.20%
发文量
1784
审稿时长
1 months
期刊介绍: Molecular Plant is dedicated to serving the plant science community by publishing novel and exciting findings with high significance in plant biology. The journal focuses broadly on cellular biology, physiology, biochemistry, molecular biology, genetics, development, plant-microbe interaction, genomics, bioinformatics, and molecular evolution. Molecular Plant publishes original research articles, reviews, Correspondence, and Spotlights on the most important developments in plant biology.
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