scholarly journals Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 741 ◽  
Author(s):  
Haihui Yang ◽  
Xiaochan Wang ◽  
Guoxiang Sun
Keyword(s):  
Intelligent Control ◽  
Point Cloud ◽  
Three Dimensional ◽  
Fruit Tree ◽  
Tree Canopy ◽  
Morphological Parameters ◽  
Kinect Sensor ◽  
Self Calibration ◽  
Morphological Measurement ◽  
Tree Width

Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards.

Three Dimensional Point Cloud Splicing of Tree Canopy Based on Multi-Source Camera

2014 ◽  
Vol 34 (12) ◽  
pp. 1215003
Author(s):  
周薇 Zhou Wei ◽  
马晓丹 Ma Xiaodan ◽  
张丽娇 Zhang Lijiao ◽  
郭彩玲 Guo Cailing ◽  
刘刚 Liu Gang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Tree Canopy

scholarly journals Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 596 ◽  
Author(s):  
Guoxiang Sun ◽  
Xiaochan Wang
Keyword(s):  
High Throughput ◽  
Point Cloud ◽  
Three Dimensional ◽  
Growth Cycle ◽  
Morphological Data ◽  
Normal Vector ◽  
Reconstruction Method ◽  
3D Point Cloud ◽  
Kinect Sensor ◽  
3D Point Cloud Reconstruction

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.

scholarly journals Fruit Morphological Measurement Based on Three-Dimensional Reconstruction

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 455 ◽  
Author(s):  
Yawei Wang ◽  
Yifei Chen
Keyword(s):  
Point Cloud ◽  
Principal Direction ◽  
Market Price ◽  
Three Dimensional ◽  
Internal Quality ◽  
Morphological Parameters ◽  
Shape Information ◽  
Dimensional Reconstruction ◽  
Iterative Closest Point Algorithm

Three-dimensional (3D) shape information is valuable for fruit quality evaluation. Grading of the fruits is one of the important postharvest tasks that the fruit processing agro-industries do. Although the internal quality of the fruit is important, the external quality of the fruit influences the consumers and the market price significantly. To solve the problem of feature size extraction in 3D fruit scanning, this paper proposes an automatic fruit measurement scheme based on a 2.5-dimensional point cloud with a Kinect depth camera. For getting a complete fruit model, not only the surface point cloud is obtained, but also the bottom point cloud is rotated to the same coordinate system, and the whole fruit model is obtained by iterative closest point algorithm. According to the centroid and principal direction of the fruit, the cut plane of the fruit is made in the x-axis, y-axis, and z-axis respectively to obtain the contour line of the fruit. The experiment is divided into two groups, the first group is various sizes of pears to get the morphological parameters; the second group is the various colors, shapes, and textures of many fruits to get the morphological parameters. Comparing the predicted value with the actual value shows that the automatic extraction scheme of the size information is effective and the methods are universal and provide a reference for the development of the related application.

Research on Landscape Environment with 3D-Reconstruction and Volume Measurement of Fruit Tree Canopy Based on Kinect

2013 ◽  
Vol 788 ◽  
pp. 480-485 ◽  
Author(s):  
Jun Mei Chen ◽  
Xiong Liu ◽  
Zuo Xi Zhao ◽  
Wei Tao Zhong
Keyword(s):  
Image Data ◽  
Point Clouds ◽  
Volume Measurement ◽  
Fruit Tree ◽  
Tree Canopy ◽  
Depth Image ◽  
Measuring Error ◽  
Kinect Sensor ◽  
Outer Envelope ◽  
Precision Management

With the demand for precision management of orchards, 3-D reconstruction of fruit tree canopy is receiving more attention. Using depth image data from Kinect sensor, this article attempts to find the 3-D coordinates of the sensed point on the canopy to get the reconstructed fruit tree canopy quickly. By slicing the point clouds to find its surface-line features and the outer envelope within the same slices is used to reconstruct the fruit tree canopy and calculate the canopy’s volume. Tests show that the measuring error for regular cuboid’s volume is about 4.2% and the repeated measuring error for citrus tree’s volume about 6.9%, indicating that applying Kinect for measuring volume of fruit tree canopy has reasonably high accuracy and reliability. The Kinect, as well as this technique, may well be used in landscape measurement on monitering.

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

A comparison between the CS-TOF and the CTA/DSA for WEB device management

2021 ◽  
pp. 159101992110147
Author(s):  
Oktay Algin ◽  
Gokhan Yuce ◽  
Ural Koc ◽  
Gıyas Ayberk
Keyword(s):  
Mr Angiography ◽  
Three Dimensional ◽  
Morphological Parameters ◽  
Perfect Agreement ◽  
Device Management ◽  
Web Device ◽  
Sensing Time ◽  
The Web

Purpose There is no study on the role of three-dimensional compressed sensing time of flight MR angiography (3D-CS-TOF) in the management of the WEB device. We evaluated the efficacy of 3-tesla 3D-CS-TOF for the management and follow-up of the WEB device implantations. Materials and methods Seventy-three aneurysms of 69 patients treated with the WEB device were retrospectively examined. Morphological parameters and embolization results of the aneurysms were assessed and compared on 3D-CS-TOF, CTA, and DSA images. Results Occluded, neck remnant, and recurrent aneurysms were observed in 61 (83.6%), 7 (9.6%), and 5 (6.8%) aneurysms, respectively. Inter- and intra-reader agreement values related to aneurysm size measurements were perfect. Aneurysms size, age, and proximal vessel tortuosity were negatively correlated with the visibility of the aneurysms and parent vessels on 3D-CS-TOF images (p = 0.043; p = 0.032; p < 0.001, respectively). Subarachnoid hemorrhage and age are associated with 3D-CS-TOF artifacts (p = 0.031; p = 0.005, respectively). 3D-CS-TOF findings are in perfect agreement with DSA or CT angiography (CTA) results (p < 0.001). Conclusion According to our results, 3D-CS-TOF can be an easy, fast, and reliable alternative for the management or follow-up of WEB assisted embolization.

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

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