3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.

中文翻译：

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基于3D模型的多光谱映射和橄榄树个性化表征的多时相监测

3D植物结构观察和表征以获取有关植物状态的全面知识仍然对精密农业（PA）构成挑战。植物冠层中复杂的分支和自我隐藏的几何形状是植被3D重建中存在的一些问题。在本文中，我们提出了一种将橄榄园的多光谱图像与高分辨率点云融合的新应用。我们的方法基于多时相方法来研究橄榄树的进化。此过程是完全自动化的，不需要人工干预即可通过多个多光谱图像捕获的反射率来表征点云。这项工作的主要目的是双重的：（1）高分辨率点云上的多光谱图像映射，以及（2）两次飞行战役中形态和光谱特征的多时相分析。最初，研究区域是通过使用无人飞行器（UAV）的高分辨率相机拍摄多个重叠的RGB图像来建模的。另外，基于无人机的多光谱传感器用于捕获某些窄带（绿色，近红外，红色和红边）的反射率。然后，通过映射为每个多光谱图像生成的反射率贴图，丰富具有高详细橄榄树几何形状的RGB点云。因此，每个3D点都与多光谱图像中相应的像素相关，在该像素中可见。结果，橄榄树的3D模型以观察到的植物冠层反射率为特征。这些反射率值也被组合起来以计算几个植被指数（NDVI，RVI，GRVI和NDRE）。根据橄榄种植园中的光谱和空间关系，对单个橄榄树进行分割。一方面，通过基于体素的3D结构分解研究植物形态，以估计植物的高度和体积。另一方面，通过检测橄榄树有意义的光谱特征来研究植物健康。此外，所提出的方法还允许处理多时间数据以研究所研究特征的可变性。因此，可以检测到一些相关的变化，并通过基于视觉的统计方法来分析每棵橄榄树的发育。