Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration
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 measurement...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/9/10/596 |
| _version_ | 1818455023678390272 |
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| author | Guoxiang Sun Xiaochan Wang |
| author_facet | Guoxiang Sun Xiaochan Wang |
| author_sort | Guoxiang Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T22:04:11Z |
| format | Article |
| id | doaj.art-75820094807c41468771fa8653bf02d7 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-12-14T22:04:11Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-75820094807c41468771fa8653bf02d72022-12-21T22:45:54ZengMDPI AGAgronomy2073-43952019-09-0191059610.3390/agronomy9100596agronomy9100596Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-CalibrationGuoxiang Sun0Xiaochan Wang1College of Engineering, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210095, ChinaPlant 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.https://www.mdpi.com/2073-4395/9/10/596three-dimensional reconstructionpoint cloud analysiskinectrgb-dgreenhouse plantshigh-throughput phenotyping |
| spellingShingle | Guoxiang Sun Xiaochan Wang Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration Agronomy three-dimensional reconstruction point cloud analysis kinect rgb-d greenhouse plants high-throughput phenotyping |
| title | Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration |
| title_full | Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration |
| title_fullStr | Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration |
| title_full_unstemmed | Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration |
| title_short | Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration |
| title_sort | three dimensional point cloud reconstruction and morphology measurement method for greenhouse plants based on the kinect sensor self calibration |
| topic | three-dimensional reconstruction point cloud analysis kinect rgb-d greenhouse plants high-throughput phenotyping |
| url | https://www.mdpi.com/2073-4395/9/10/596 |
| work_keys_str_mv | AT guoxiangsun threedimensionalpointcloudreconstructionandmorphologymeasurementmethodforgreenhouseplantsbasedonthekinectsensorselfcalibration AT xiaochanwang threedimensionalpointcloudreconstructionandmorphologymeasurementmethodforgreenhouseplantsbasedonthekinectsensorselfcalibration |