School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China.
Sensors (Basel). 2021 Mar 23;21(6):2247. doi: 10.3390/s21062247.
It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.
自动测量植物仍然是一项具有挑战性的任务。提出了一种基于手持三维（3D）激光扫描仪的自动植物测量新方法。该方法的目的是从不同遮挡的活体植物中自动选择典型叶片样本并估算其形态特征。该方法主要包括数据采集和处理。数据采集是通过手持 3D 激光扫描仪（ZGScan 717，中国武汉中关自动化技术公司制造）实时获取植物高精度的 3D 网格模型。数据处理主要包括基于多级区域生长分割方法的典型叶片样本提取和形态特征估计，该方法使用两个叶片形状模型。可以自动估计四个与尺度相关的特征和六个相应的尺度不变特征。对四组不同树冠遮挡的植物进行了实验。实验结果表明，对于具有不同树冠遮挡的植物，94.02%的典型叶片样本可以很好地扫描，87.61%的典型叶片样本可以自动提取。自动估算的形态特征与手动测量值 EF（建模效率）高度相关，对于与尺度相关的特征，EF 大于 0.8919，对于尺度不变特征，EF 大于 0.7434）。测量一株植物的平均时间为 196.37 秒（数据扫描用时 186.08 秒，3D 植物模型输出用时 5.95 秒，数据处理用时 4.36 秒）。该方法对不同树冠遮挡的植物具有较强的鲁棒性和较低的时间成本，具有实时植物测量和高通量植物表型分析的潜在应用价值。
