Abstract
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.
Highlights
The growth, yield, and quality of crops depend on a variety of nutrient elements, among which nitrogen has the most significant impact [1,2]
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
To manipulate the plant chlorophyll levels, 60 tomato plant samples were divided into five groups that received five different doses of nutrient solution, i.e., 25%, 75%, 100%, 150%, or 200% of the standard formula [33], with 100% being the dose required for normal tomato growth [34]; each group included 12 plants
Summary
The growth, yield, and quality of crops depend on a variety of nutrient elements, among which nitrogen has the most significant impact [1,2]. The construction of a multispectral 3D point cloud model is inefficient, because plant images from dozens of AOVs are required This technology is unsuitable for high-throughput chlorophyll measurement. CIG (RCIG), normalized green index (NG), normalized red index (NR), and green RVI (GRVI), were calculated and combined with the chlorophyll SPAD values to establish highly efficient prediction models and to measure the chlorophyll content in the plant canopy accurately. This measurement method can be expanded to assess the physiological features of other plant canopies. This method provides good technical support for high-throughput plant phenotypic analysis and is significant for the development of plant phenomics and other research fields
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