Graduate School, University of Tokyo, Tokyo 113-8657, Japan.
Sensors (Basel). 2018 Oct 22;18(10):3576. doi: 10.3390/s18103576.
Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management.
自动、高效的植物监测可实现精准的植物管理。构建植物的三维（3D）模型并获取其空间信息是获取植物结构参数的有效方法。在此，我们从不同位置拍摄的多个场景构建了叶片的 3D 图像，并对其进行自动分割，以自动检索叶片面积和倾斜角度。首先，进行初始分割时，从顶部观察叶片，然后使用距离变换和分水岭算法对顶视图中的叶片进行分割。接下来，将初始分割后的叶片图像缩小 90%，并为每个叶片生成种子区域。将种子区域重新投影到 3D 图像上，然后使用 3D 信息扩展种子区域来分割每个叶片。叶片分割后，通过体素计算精确估计每个叶片的叶片面积及其倾斜角度。结果，经过自动叶片分割后，叶片面积和叶片倾斜角度的估计值准确。这种自动植物结构分析方法可实现精确、高效的植物培育和生长管理。
