Abstract
Plant morphological data are an important basis for precision agriculture and plant phenomics. The three-dimensional (3D) geometric shape of plants is complex, and the 3D morphology of a plant changes relatively significantly during the full growth cycle. In order to make high-throughput measurements of the 3D morphological data of greenhouse plants, it is necessary to frequently adjust the relative position between the sensor and the plant. Therefore, it is necessary to frequently adjust the Kinect sensor position and consequently recalibrate the Kinect sensor during the full growth cycle of the plant, which significantly increases the tedium of the multiview 3D point cloud reconstruction process. A high-throughput 3D rapid greenhouse plant point cloud reconstruction method based on autonomous Kinect v2 sensor position calibration is proposed for 3D phenotyping greenhouse plants. Two red–green–blue–depth (RGB-D) images of the turntable surface are acquired by the Kinect v2 sensor. The central point and normal vector of the axis of rotation of the turntable are calculated automatically. The coordinate systems of RGB-D images captured at various view angles are unified based on the central point and normal vector of the axis of the turntable to achieve coarse registration. Then, the iterative closest point algorithm is used to perform multiview point cloud precise registration, thereby achieving rapid 3D point cloud reconstruction of the greenhouse plant. The greenhouse tomato plants were selected as measurement objects in this study. Research results show that the proposed 3D point cloud reconstruction method was highly accurate and stable in performance, and can be used to reconstruct 3D point clouds for high-throughput plant phenotyping analysis and to extract the morphological parameters of plants.
Highlights
The phenotype of a plant is determined or affected by genetic and environmental factors, and the structure, composition, and physical, physiological, and biochemical traits and properties of the plant reflect these factors during growth and development and at maturity [1]
It is necessary to frequently adjust the Kinect sensor position and recalibrate the Kinect sensor during the full growth cycle of the plant, which significantly increases the tedium of the multiview 3D point cloud reconstruction process
To examine the performance of the proposed 3D point cloud reconstruction method, 60 greenhouse tomato plants (GTPs) were selected as measurement objects
Summary
The phenotype of a plant is determined or affected by genetic and environmental factors, and the structure, composition, and physical, physiological, and biochemical traits and properties of the plant reflect these factors during growth and development and at maturity [1]. Plant phenotypic data are an important basis for analyzing the relationship between genotype, environment, and phenotype. Plant phenotyping techniques severely lag behind research needs and have become a bottleneck that limits the development of molecular crop breeding and functional plant genomics [2,3,4]. With the rapid development of sensor and spectral imaging technologies, automated plant phenotyping can be achieved by computer graphic and image processing. Studying this technique is of great significance to the realization of the high-throughput, precise, and automated phenotyping of greenhouse plants [5,6].
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