Nondestructive detection of radish root phenotype based on electrical resistance tomography and ResNet

XIAO Shuyuan; HE Wei; LU Wei

doi:10.7671/j.issn.1001-411X.202205041

Journal of South China Agricultural University > 2023 > 44(1): 102-109. > DOI: 10.7671/j.issn.1001-411X.202205041

XIAO Shuyuan, HE Wei, LU Wei. Nondestructive detection of radish root phenotype based on electrical resistance tomography and ResNet[J]. Journal of South China Agricultural University, 2023, 44(1): 102-109. DOI: 10.7671/j.issn.1001-411X.202205041

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Nondestructive detection of radish root phenotype based on electrical resistance tomography and ResNet

XIAO Shuyuan^1,,
HE Wei¹,
LU Wei^2, ,

1.
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
2.
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

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Received Date: May 22, 2022
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
In view of the problems that the existing experimental equipment for root phenotype detection is expensive, requires special personnel to operate, and cannot perform rapid in situ non-destructive detection of root phenotypes, this paper proposes a non-destructive detection method for radish root phenotype based on electrical resistance tomography (ERT) and deep residual network (ResNet).

Method
Firstly, the ERT positive problem of the radish-agar field for different cases was performed by COMSOL, and a large amount of boundary voltage data was obtained. Secondly, the nonlinear mapping relationship between the internal conductivity distribution and the boundary voltage inside the radish-agar field was modeled based on ResNet, and image of radish-agar field were reconstructed. Finally, a set of radish root phenotype detection device was developed based on ERT, and the experimental verification was carried out.

Result
The radish root phenotype detection method based on ERT and ResNet could achieve sustainable nondestructive detection of radish root phenotype, and the experimental setup was simple to operate and low cost, while the relative error of image reconstruction was less than 5%.

Conclusion
The method of radish root phenotype detection based on ERT can achieve nondestructive detection of radish root phenotype with high imaging accuracy combining with ResNet algorithm. This method can be effectively used for the detection of radish root phenotypes.
- Electrical resistance tomography,
- Radish root system,
- ResNet,
- Nondestructive detection

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References (22)

References

[1]	刘志芳. 白萝卜的开发价值及其栽培技术[J]. 种子科技, 2019, 37(11): 63-64. doi: 10.3969/j.issn.1005-2690.2019.11.047
[2]	HE W, CHEN Y, YIN Z. Adaptive neural network control of an uncertain robot with full-state constraints[J]. IEEE Transactions on Cybernetics, 2016, 46(3): 620-629. doi: 10.1109/TCYB.2015.2411285
[3]	魏林, 梁志怀. 白萝卜肉质根开裂的主要原因及其防治方法[J]. 长江蔬菜, 2017(3): 48-49.
[4]	HUDEK C, STURROCK C J, ATKINSON B S, et al. Root morphology and biomechanical characteristics of high altitude alpine plant species and their potential application in soil stabilization[J]. Ecological Engineering, 2017, 109: 228-239. doi: 10.1016/j.ecoleng.2017.05.048
[5]	李燕丽, 王昌昆, 卢碧林, 等. 基于微根管技术的盐胁迫下小麦根系生长原位监测方法[J]. 土壤学报, 2021, 58(3): 599-609.
[6]	ZHAO X, XING L, SHEN S, et al. Non-destructive 3D geometric modeling of maize root-stubble in-situ via X-ray computed tomography[J]. International Journal of Agricultural and Biological Engineering, 2020, 13(3): 174-179. doi: 10.25165/j.ijabe.20201303.5268
[7]	PFLUGFELDER D, KOCHS J, KOLLER R, et al. The root system architecture of wheat establishing in soil is associated with varying elongation rates of seminal roots: Quantification using 4D magnetic resonance imaging[J]. Journal of Experimental Botany, 2022, 73(7): 2050-2060. doi: 10.1093/jxb/erab551
[8]	PFLUGFELDER D, METZNER R, VAN DUSSCHOTEN D, et al. Non-invasive imaging of plant roots in different soils using magnetic resonance imaging (MRI)[J]. Plant Methods, 2017, 13: 102. doi: 10.1186/s13007-017-0252-9.
[9]	徐汇, 徐元元, 李光辉. 基于探地雷达的果树根系检测试验与分析[J]. 江苏农业科学, 2022, 50(2): 170-177. doi: 10.15889/j.issn.1002-1302.2022.02.029
[10]	颜华, 胡丽娟, 王伊凡, 等. 基于改进灵敏度矩阵的ERT图像重建[J]. 仪器仪表学报, 2018, 39(5): 241-248. doi: 10.19650/j.cnki.cjsi.J1703034
[11]	LI F, TAN C, DONG F. Electrical resistance tomography image reconstruction with densely connected convolutional neural network[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 4500811. doi: 10.1109/TIM.2020.3013056.
[12]	SONG Z, WEN J, WAN N, et al. An optimization algorithm H-GVSPM for electrical impedance tomography[J]. IEEE Sensors Journal, 2022: 1-8. doi: 10.1109/JSEN.2022.3170838.
[13]	王兴龙, 王立海. 白桦模拟缺陷材的电阻层析成像规律探究[J]. 林业工程学报, 2016, 1(2): 106-111.
[14]	徐梓敬, 贾培, 吴楠, 等. GA-SVM在木材缺陷识别中的应用[J]. 传感器与微系统, 2019, 38(9): 153-156. doi: 10.13873/J.1000-9787(2019)09-0153-04
[15]	段国秀, 贾小旭, 白晓, 等. 电阻率层析成像法在土壤水文学中的应用: 基于CiteSpace的计量分析[J]. 土壤通报, 2021, 52(6): 1447-1459.
[16]	盛丰, 文鼎, 熊祎玮, 等. 基于电阻率层析成像技术的农田土壤优先流原位动态监测[J]. 农业工程学报, 2021, 37(8): 117-124. doi: 10.11975/j.issn.1002-6819.2021.08.013
[17]	WEIGAND M, KEMNA A. Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems[J]. Biogeosciences, 2017, 14(4): 921-939. doi: 10.5194/bg-14-921-2017
[18]	CORONA D D J, SOMMER S, ROLFE S A, et al. Electrical impedance tomography as a tool for phenotyping plant roots[J]. Plant Methods, 2019, 15: 49. doi: 10.1186/s13007-019-0438-4.
[19]	李峰, 谭超, 董峰. 全连接深度网络的电学层析成像算法[J]. 工程热物理学报, 2019, 40(7): 1526-1531.
[20]	许辰航, 陈继明, 刘伟楠, 等. 基于深度残差网络的GIS局部放电PRPD谱图模式识别[J]. 高电压技术, 2022, 48(3): 1113-1123. doi: 10.13336/j.1003-6520.hve.20201663
[21]	LI B, WANG J M, WANG Q, et al. An improved generalized finite element method for electrical resistance tomography forward model[J]. Journal of Electrical Engineering & Technology, 2019, 14(6): 2595-2606.
[22]	仝卫国, 曾世超, 张立峰, 等. 基于深度残差神经网络的电阻层析成像及流型辨识方法[J]. 系统仿真学报, 2022, 34(9): 2028-2036. doi: 10.16182/j.issn1004731x.joss.21-0294

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