Abstract
In recent years, three-dimensional (3D) data acquisition and model reconstruction of plants have been developed as a hot topic of plant scientific researches. However, the morphological structure of plants is very complex and it is hard to describe the details. The data acquisition approaches are diverse for different parts of plants. This study introduces the data acquisition methods of different scales of maize. The grain, leaf and ear, individual plant and maize colony represents the target models for different scales. 3D data acquisition instruments are used to acquire the morphometric of each target. It is found that the organ scale is the simplest to obtain and process. The smallest grain needs high-resolution scanner to acquire the morphological details, while the plant canopy is the hardest one for point cloud process and modeling. The data and reconstructed models are oriented to digital plant, phenotyping analysis, FSPMs research, and popular science education application.
Highlights
Because image acquisition only depends on cameras, multi-view stereo (MVS) approach is much cheaper than 3D scanning with general applicability
Because of the 3D recovery algorithms, MVS has a lower accuracy than 3D scanning and is relatively suitable for organs [24] and individual plants [25]
We introduced multi-scale 3D data acquisition approaches of maize, including what scanners were suitable for the acquisition, how to build the scanning scene, the accuracy and efficiency of each scale, and the visualization results of the point clouds
Summary
The intersection of information science and traditional agricultural science has become a powerful approach to scientific and technological innovation. Analyze, evaluate, simulate and predict the production and life system of plants, it is necessary to analyze plant morphological structure characteristics and construct three-dimensional (3D) model through measured in situ morphological structure data by using 3D scanning technology. 3D point cloud based analysis and reconstruction [1] is one important content of digital plant [2], functional-structural plant modeling [3], and plant phenotyping [4] research. It is a challenge to obtain high-quality 3D point clouds of plant shape at different scales and to produce realistic 3D models of plant. Rapid and accurate 3D morphological data acquisition, analysis and reconstruction [5] of maize is of great significance for maize genotype-phenotype analysis [6], canopy photosynthetic productivity calculation [7] and plant type or density optimization [8]
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