A method of 3D nondestructive detection for plant root in situ based on CBCT imaging

Zhou, Xuecheng; Cao, Xia; Zhang, Changling; Yan, Hui; Li, Yihai; Luo, Xiwen

doi:10.1109/bmei.2014.7002753

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Previous studies have verified that the average diameter is an important index of root composition and function activity, and root penetration is related to root diameter and rigidity (Clark et al., 2008; Zhang et al., 2005). With the development of root analysis methods, various quantitative studies of root system growth are now possible (Cao et al., 2012; Le Marié et al., 2014; Li et al., 2015; Lu et al., 2018; Yu et al., 2016; Zhou et al., 2015). In this paper, we observed the morphological characteristics of L LR , AD LR , SA LR , and V LR under different treatments by combining tube cultivation sampling and image scanning analysis.…”

Section: Discussionmentioning

confidence: 99%

Biomass‐based lateral root morphological parameter models for rapeseed (Brassica napus L.)

Zhang,

Wu,

Sun

et al. 2023

Food and Energy Security

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Lateral roots, including adventitious roots, are the main component of rapeseed roots with support, absorb, and synthesis functions and their morphological parameters directly affecting the plant's aboveground growth and yield. Root biomass, as a material base for lateral root growth, can be used as a link between plant phenotypes and their physiological processes, as well as to enhance root 3D growth model mechanisms and accuracy. To quantify the relationships between lateral root morphological indices and the corresponding organ biomass for rapeseed, we used two cultivars, NY 22 (conventional) and NZ 1818 (hybrid), and conducted cultivar and fertilizing cylindrical tube experiments during the 2016–2019, with two fertilizer levels, no fertilizer, and 180 kg N ha−1 fertilizer. The lateral root biomass and morphological parameters were determined during the whole growth period. The biomass‐based lateral root morphological parameter models were developed by analyzing the quantitative relationship between the lateral root morphological indices and their corresponding biomass, and the descriptive models were verified with independent experimental data. The results showed that the correlation (r) of simulated and observed values for the lateral root morphological parameters are all greater than 0.9 with significant levels at p < 0.001. The absolute values of the average absolute difference (da) of simulated and observed values for the lateral root length (LLR), lateral root average diameter (ADLR), lateral root surface area (SALR), and lateral root volume (VLR) are −30.408 cm, −0.003 mm, 12.902 cm2, and 0.039 cm3, respectively. The RMSE values are 175.183 cm, 0.010 mm, 59.710 cm2, and 1.513 cm3, respectively. The ratio of da to the average observed values (dap) for the LLR and VLR are all less than 5%, and the ADLR and SALR are all <6%. The models developed in this paper have good performance and reliability for predicting lateral root morphological parameters of rapeseed. The study provides a mechanistic method for linking the rapeseed growth model with the morphological model using corresponding organic biomass and laying a good foundation for establishing a 3D morphological model for rapeseed root system based on biomass.

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Section: Discussionmentioning

confidence: 99%

Biomass‐based lateral root morphological parameter models for rapeseed (Brassica napus L.)

Zhang,

Wu,

Sun

et al. 2023

Food and Energy Security

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“…It would also enable models to create accurate representations of live plants. Several methods have been made to perform root imaging, such as the minirhizotron technique (Huang et al 1992), X-ray computed tomography (Zhou et al 2014), and the use of rhizoboxes (Jia et al 2019). These methods involve taking images of the root system through various means, then utilizing software and machine learning to analyze the images for traits being investigated.…”

Section: Introductionmentioning

confidence: 99%

Acquisition of 3D Root System Simulation Parameters Using 2D Extracted Image Data and Genetic Programming

Custodio¹,

Leon²,

Galindo³

et al. 2022

Philipp J Sci

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Root studies like propagation and morphological traits unlock a higher-level understanding of plants to better the growth of agricultural crops and maximize farm yields. As they are located underground, there is an additional challenge in elucidating their structure and behavior. Root imaging allows for real-time observations and there are several known methods. One imaging dataset simulated entire 3D root systems to create 2D images for data analysis and measurements. In this study, the dataset’s extracted measurements will be used to reconstruct the true parameters of the simulated 3D root system through the use of multigene symbolic regression genetic programming (MSRGP). Eleven (11) parameters were selected as the output variables, each for monocot and dicot data for a total of 22 MSRGP models. Among these, thirteen of them showed high R2 values greater than 80%, proving the high accuracy of the MSRGP method. Input variables that were frequently used across multiple models were also noted, such as tip count, exploration ratio, and area. In addition to the accuracy that MSRGP provides in predicting variables, the computation time of these models is found to be as low as a few milliseconds. Once trained, this speed allows the models to be integrated into relevant applications without significant increases in computation cost.

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“…Mooney et al [27] reviewed the recent research on three-dimensional root reconstruction based on CT images. Zhou et al [45] proposed a threedimensional visualization and nondestructive detection technique using XCT based on a flat panel detector. Based on root dynamic growth characteristics and their development processes, many researchers have constructed 3D modeling dynamic growth simulations of crop root systems while conducting research into visual simulations of crop root growth processes.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Visualization of Rice Roots Based on Morphological Parameters

2020

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As an important organ of absorbing water and nutrients for rice, the cognition and expression of the three-dimensional (3D) morphology of roots has become a bottleneck in-depth study of rice roots due to the observing obstacles of soil. To clarify the morphological and distributional characteristics of rice roots, ''root box'' experiments are conducted to mensurate three dimensional spatial coordinates and morphological parameters of rice roots at different growth stages. Based on these experiments and the morphological parameters, the initial position of the root node and growth direction of branches are determined, a rice root model is constructed with B-spline curves by analyzing the topological structure of rice roots to quantify the biological characteristics of rice roots and summarize the morphological structure and growth characteristics of rice roots and improve the cubic growth function to describe the growth change in rice roots. Meanwhile, the output accuracy of the model is tested. In addition, dynamic simulation of rice root growth characteristics in 3D space is implemented using Visual C++ and the OpenGL standard graphics library. Analysis results indicate that the average simulated fitness of the total root length, surface area and volume is about 94.82%, 93.86%, and 91.96%, respectively. Therefore, the model can express the morphological characteristics and growth rules of rice roots at different growth stages, which enrich the digitization and visualization methods for roots of other crops. INDEX TERMS B-spline curve, morphological parameters, rice roots, visualization.

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A method of 3D nondestructive detection for plant root in situ based on CBCT imaging

Cited by 6 publications

References 13 publications

Biomass‐based lateral root morphological parameter models for rapeseed (Brassica napus L.)

Biomass‐based lateral root morphological parameter models for rapeseed (Brassica napus L.)

Acquisition of 3D Root System Simulation Parameters Using 2D Extracted Image Data and Genetic Programming

Modeling and Visualization of Rice Roots Based on Morphological Parameters

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