College of Information Science and Technology, Donghua University, Shanghai 201620, China.
Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China.
Sensors (Basel). 2018 Oct 25;18(11):3625. doi: 10.3390/s18113625.
Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided into three steps: (1) point cloud pre-processing, (2) facet over-segmentation, and (3) facet region growing for individual leaf segmentation. The experimental results show that the proposed method is effective and efficient in segmenting individual leaves from 3D point clouds of greenhouse ornamentals such as , , and , and the average precision and recall are both above 90%. The results also reveal the wide applicability of the proposed methodology for point clouds scanned from different kinds of 3D imaging systems, such as stereo vision and Kinect v2. Moreover, our method is potentially applicable in a broad range of applications that aim at segmenting regular surfaces and objects from a point cloud.
叶子占大多数植物所有器官面积的最大比例，是植物中具有光合作用活性物质的主要部分。观察单个叶子可以帮助识别其生长状态并测量复杂的表型特征。由于物种受到高度限制以及冠层遮挡的脆弱性，目前基于图像的叶子分割方法存在问题。在这项工作中，我们提出了一种使用面元过度分割和面元区域生长的密集植物点云的单个叶子分割方法。该方法可以分为三个步骤：（1）点云预处理，（2）面元过度分割，以及（3）用于单个叶子分割的面元区域生长。实验结果表明，该方法在分割温室观赏植物（如、和）的三维点云中是有效和高效的，平均精度和召回率均在 90%以上。结果还表明，该方法适用于从不同类型的三维成像系统（如立体视觉和 Kinect v2）扫描的点云。此外，我们的方法可能适用于从点云中分割规则表面和对象的广泛应用。
