Making Use of 3D Models for Plant Physiognomic Analysis: A Review

Paturkar, Abhipray; Gupta, Gourab Sen; Bailey, Donald G.

doi:10.3390/rs13112232

Cited by 20 publications

(15 citation statements)

References 143 publications

(183 reference statements)

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“…In further research, the rapid acquisition of crop phenotypic data and the rapid modeling of crops should be focused on. This may be achieved by connecting 3D scanning software [47,48] to the GroIMP modelling platform. In this way, it can provide basic conditions for the simulation study of crop dynamic growth model, so as to realize higher precision agricultural management.…”

Section: Discussionmentioning

confidence: 99%

Determination of the Optimal Orientation of Chinese Solar Greenhouses Using 3D Light Environment Simulations

Liu

Henke

et al. 2022

Remote Sensing

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With the continuous use of resources, solar energy is expected to be the most used sustainable energy. To improve the solar energy efficiency in Chinese Solar Greenhouses (CSG), the effect of CSG orientation on intercepted solar radiation was systematically studied. By using a 3D CSG model and a detailed crop canopy model, the light environment within CSG was optimized. Taking the most widely used Liao-Shen type Chinese solar greenhouse (CSG-LS) as the prototype, the simulation was fully verified. The intercepted solar radiation of the maintenance structures and crops was used as the evaluation index. The results showed that the highest amount of solar radiation intercepted by the maintenance structures occurred in the CSG orientations of 4–6° south to west (S-W) in 36.8° N and 38° N areas, 8–10° S-W in 41.8° N areas, and 2–4° south to east (S-E) in 43.6° N areas. The solar radiation intercepted by the crop canopy displayed the highest value at an orientation of 2–4° S-W in 36.8° N, 38° N, 43.6° N areas, and 4–6° S-W in the 41.8° N area. Furthermore, the proposed model could provide scientific guidance for greenhouse crop modelling.

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Section: Discussionmentioning

confidence: 99%

Determination of the Optimal Orientation of Chinese Solar Greenhouses Using 3D Light Environment Simulations

Liu

Henke

et al. 2022

Remote Sensing

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“…Using routines from standard data processing software libraries such as MATLAB [138], OpenCV [139,140], or the Point Cloud Library [141,142], primary traits can be extracted. For example, after cutting the point cloud to the region of interest and performing a data cleaning step, non-complex parameters such as height and width can be derived [143]. Machine learning approaches can then be employed for further processing, such as segmenting plant organs such as leaves, stems, and flowers [117,[144][145][146].…”

Section: Data Processing Of 3d Phenotypingmentioning

confidence: 99%

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

et al. 2023

Plants

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Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.

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“…Three-dimensional (3D) data, describing the spatial information of the target, is widely used for plant phenotyping [ 4 ]. 3D data can be acquired through time-of-flight techniques, such as lidar, laser scanners, and depth cameras, or stereo vision techniques, such as binocular cameras and multiview cameras [ 5 , 6 ]. Since spectral images and 3D data are highly complementary in presenting information about plant growth and development, analyzing on the fusion of multimodal data (data acquired by different sensors) can provide insights into high-throughput plant phenotyping.…”

Section: Introductionmentioning

confidence: 99%

Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images

Xie

et al. 2023

Plant Phenomics

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Accurate and high-throughput plant phenotyping is important for accelerating crop breeding. Spectral imaging that can acquire both spectral and spatial information of plants related to structural, biochemical, and physiological traits becomes one of the popular phenotyping techniques. However, close-range spectral imaging of plants could be highly affected by the complex plant structure and illumination conditions, which becomes one of the main challenges for close-range plant phenotyping. In this study, we proposed a new method for generating high-quality plant 3-dimensional multispectral point clouds. Speeded-Up Robust Features and Demons was used for fusing depth and snapshot spectral images acquired at close range. A reflectance correction method for plant spectral images based on hemisphere references combined with artificial neural network was developed for eliminating the illumination effects. The proposed Speeded-Up Robust Features and Demons achieved an average structural similarity index measure of 0.931, outperforming the classic approaches with an average structural similarity index measure of 0.889 in RGB and snapshot spectral image registration. The distribution of digital number values of the references at different positions and orientations was simulated using artificial neural network with the determination coefficient ( R 2 ) of 0.962 and root mean squared error of 0.036. Compared with the ground truth measured by ASD spectrometer, the average root mean squared error of the reflectance spectra before and after reflectance correction at different leaf positions decreased by 78.0%. For the same leaf position, the average Euclidean distances between the multiview reflectance spectra decreased by 60.7%. Our results indicate that the proposed method achieves a good performance in generating plant 3-dimensional multispectral point clouds, which is promising for close-range plant phenotyping.

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Making Use of 3D Models for Plant Physiognomic Analysis: A Review

Cited by 20 publications

References 143 publications

Determination of the Optimal Orientation of Chinese Solar Greenhouses Using 3D Light Environment Simulations

Determination of the Optimal Orientation of Chinese Solar Greenhouses Using 3D Light Environment Simulations

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images

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