Li Xumeng, Wang Xiaohui, Wei Hailin, Zhu Xinguang, Peng Yulin, Li Ming, Li Tao, Huang Huang
Agricultural Mathematical Modeling and Data Processing Center, Hunan Agricultural University, Changsha, China.
International Rice Research Institute, Metro Manila, Philippines.
PLoS One. 2017 May 30;12(5):e0177205. doi: 10.1371/journal.pone.0177205. eCollection 2017.
This study developed a technique system for the measurement, reconstruction, and trait extraction of rice canopy architectures, which have challenged functional-structural plant modeling for decades and have become the foundation of the design of ideo-plant architectures. The system uses the location-separation-measurement method (LSMM) for the collection of data on the canopy architecture and the analytic geometry method for the reconstruction and visualization of the three-dimensional (3D) digital architecture of the rice plant. It also uses the virtual clipping method for extracting the key traits of the canopy architecture such as the leaf area, inclination, and azimuth distribution in spatial coordinates. To establish the technique system, we developed (i) simple tools to measure the spatial position of the stem axis and azimuth of the leaf midrib and to capture images of tillers and leaves; (ii) computer software programs for extracting data on stem diameter, leaf nodes, and leaf midrib curves from the tiller images and data on leaf length, width, and shape from the leaf images; (iii) a database of digital architectures that stores the measured data and facilitates the reconstruction of the 3D visual architecture and the extraction of architectural traits; and (iv) computation algorithms for virtual clipping to stratify the rice canopy, to extend the stratified surface from the horizontal plane to a general curved surface (including a cylindrical surface), and to implement in silico. Each component of the technique system was quantitatively validated and visually compared to images, and the sensitivity of the virtual clipping algorithms was analyzed. This technique is inexpensive and accurate and provides high throughput for the measurement, reconstruction, and trait extraction of rice canopy architectures. The technique provides a more practical method of data collection to serve functional-structural plant models of rice and for the optimization of rice canopy types. Moreover, the technique can be easily adapted for other cereal crops such as wheat, which has numerous stems and leaves sheltering each other.
本研究开发了一种用于水稻冠层结构测量、重建和特征提取的技术系统,几十年来,水稻冠层结构一直是功能-结构植物建模的挑战所在,并且已成为理想株型设计的基础。该系统采用定位-分离-测量方法(LSMM)收集冠层结构数据,并采用解析几何方法对水稻植株的三维(3D)数字结构进行重建和可视化。它还使用虚拟裁剪方法来提取冠层结构的关键特征,如空间坐标中的叶面积、倾角和方位分布。为建立该技术系统,我们开发了:(i)用于测量茎轴空间位置和叶中脉方位以及获取分蘖和叶片图像的简单工具;(ii)用于从分蘖图像中提取茎直径、叶节点和叶中脉曲线数据以及从叶片图像中提取叶长、叶宽和叶形数据的计算机软件程序;(iii)一个数字结构数据库,用于存储测量数据并便于3D视觉结构的重建和结构特征的提取;以及(iv)用于虚拟裁剪的计算算法,以对水稻冠层进行分层,将分层表面从水平面扩展到一般曲面(包括圆柱面),并在计算机上实现。该技术系统的每个组件都经过了定量验证,并与图像进行了视觉比较,同时分析了虚拟裁剪算法的灵敏度。该技术成本低廉且准确,为水稻冠层结构的测量、重建和特征提取提供了高通量。该技术提供了一种更实用的数据收集方法,以服务于水稻的功能-结构植物模型并优化水稻冠层类型。此外,该技术可以很容易地适用于其他谷类作物,如小麦,小麦有许多相互遮蔽的茎和叶。