Development of High-resolution 3D Phenotyping System Using Depth Camera and RGB Camera

Yang, Myongkyoon; Cui, Jinshi; Jeong, Eun Seong; Cho, Seong In

doi:10.13031/aim.201700948

Cited by 3 publications

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“…Acknowledgments: This research was supported by Smart Agriculture Innovation Center of Kyungpook National University, Republic of Korea. This article consisted of reconstructed and developed parts of the research presented at the conference [50].…”

Section: Conflicts Of Interestmentioning

confidence: 99%

High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

Yang

Cho

2021

Agriculture

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Beyond the use of 2D images, the analysis of 3D images is also necessary for analyzing the phenomics of crop plants. In this study, we configured a system and implemented an algorithm for the 3D image reconstruction of red pepper plant (Capsicum annuum L.), as well as its automatic analysis. A Kinect v2 with a depth sensor and a high-resolution RGB camera were used to obtain more accurate reconstructed 3D images. The reconstructed 3D images were compared with conventional reconstructed images, and the data of the reconstructed images were analyzed with respect to their directly measured features and accuracy, such as leaf number, width, and plant height. Several algorithms for image extraction and segmentation were applied for automatic analysis. The results showed that the proposed method showed an error of about 5 mm or less when reconstructing and analyzing 3D images, and was suitable for phenotypic analysis. The images and analysis algorithms obtained by the 3D reconstruction method are expected to be applied to various image processing studies.

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Section: Conflicts Of Interestmentioning

confidence: 99%

High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

Yang

Cho

2021

Agriculture

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“…Li et al [39] developed a technique system for the measurement, reconstruction, and trait extraction of rice canopy architectures. The method of using Kinect and turntable to perform phenotypic information for indoor plants is necessary to rotate the turntable multiple times interval at a fixed angle [40][41][42][43], and then use the point cloud registering algorithm for 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

Wang

Chen

2020

Plants

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In agriculture, information about the spatial distribution of plant growth is valuable for applications. Quantitative study of the characteristics of plants plays an important role in the plants’ growth and development research, and non-destructive measurement of the height of plants based on machine vision technology is one of the difficulties. We propose a methodology for three-dimensional reconstruction under growing plants by Kinect v2.0 and explored the measure growth parameters based on three-dimensional (3D) point cloud in this paper. The strategy includes three steps—firstly, preprocessing 3D point cloud data, completing the 3D plant registration through point cloud outlier filtering and surface smooth method; secondly, using the locally convex connected patches method to segment the leaves and stem from the plant model; extracting the feature boundary points from the leaf point cloud, and using the contour extraction algorithm to get the feature boundary lines; finally, calculating the length, width of the leaf by Euclidean distance, and the area of the leaf by surface integral method, measuring the height of plant using the vertical distance technology. The results show that the automatic extraction scheme of plant information is effective and the measurement accuracy meets the need of measurement standard. The established 3D plant model is the key to study the whole plant information, which reduces the inaccuracy of occlusion to the description of leaf shape and conducive to the study of the real plant growth status.

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Integrating Depth and Image Sequences for Planetary Rover Mapping Using RGB-D Sensor

Peng

Wan

Xing³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:RGB-D camera allows the capture of depth and color information at high data rates, and this makes it possible and beneficial integrate depth and image sequences for planetary rover mapping. The proposed mapping method consists of three steps. First, the strict projection relationship among 3D space, depth data and visual texture data is established based on the imaging principle of RGB-D camera, then, an extended bundle adjustment (BA) based SLAM method with integrated 2D and 3D measurements is applied to the image network for high-precision pose estimation. Next, as the interior and exterior elements of RGB images sequence are available, dense matching is completed with the CMPMVS tool. Finally, according to the registration parameters after ICP, the 3D scene from RGB images can be registered to the 3D scene from depth images well, and the fused point cloud can be obtained. Experiment was performed in an outdoor field to simulate the lunar surface. The experimental results demonstrated the feasibility of the proposed method * Corresponding author.

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Development of High-resolution 3D Phenotyping System Using Depth Camera and RGB Camera

Cited by 3 publications

References 0 publications

High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

Integrating Depth and Image Sequences for Planetary Rover Mapping Using RGB-D Sensor

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