College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology & Equipment, Nanjing 210031, China.
Sensors (Basel). 2019 Jul 30;19(15):3345. doi: 10.3390/s19153345.
Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.
无损植物生长测量对于研究植物生长和健康至关重要。一种用于获取植物信息的无损测量系统包括形态和生理信息的测量，但大多数系统使用两个独立的测量系统来测量这两种特征。在本研究中，设计了一种用于温室番茄植株的高度集成、多光谱、三维（3D）无损测量系统。该系统使用了 Kinect 传感器、SOC710 高光谱成像仪、电动旋转台等组件。提出了一种基于傅里叶变换的异质传感图像配准技术，用于将 SOC710 多光谱反射率配准到 Kinect 深度图像坐标系中。此外，还开发了一种基于 Kinect 传感器姿态估计和自标定的 3D 多视图 RGB-D 图像重建方法，用于重建番茄植株的多光谱 3D 点云模型。进行了一项实验来测量植物冠层的叶绿素，并通过基于 3D 多光谱点云模型和单视图点云模型的土壤和植物分析器开发（SPAD）测量模型来测量相对叶绿素含量，并对其性能进行了比较和分析。结果表明，使用多视图点云模型的特征变量建立的测量模型优于使用单视图点云模型的变量建立的模型。因此，多光谱 3D 重建方法能够重建植物多光谱 3D 点云模型，优化了传统的基于二维图像的 SPAD 测量方法，可以实现对植物叶绿素的精确、高效的高通量测量。
