PDF(3284 KB)
Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks
Pang Yuanen, Wang Zhicheng, Li Xu, Du Saizhao
PDF(3284 KB)
PDF(3284 KB)
Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks
The moisture content of the soil is the main factor affecting the quality of fine-grained soil. Rapid recognition of soil surface moisture content is an urgent need for developing intelligent monitoring and construction technology in agricultural and geotechnical engineering. In order to overcome the limitation that traditional water content measurement or monitoring methods cannot meet the real-time nondestructive monitoring of soil surface moisture content, an intelligent moisture content recognition algorithm based on the image is developed. Firstly, we collected surface photos of 4 different types of soils under different moisture contents in the laboratory and obtained a high-quality sample library of more than 1 400 pictures, which laid a data foundation for machine learning model construction. Then the classical convolutional neural network is used to learn the image dataset of soil moisture content, and the intelligent recognition model of soil moisture content is established. The model comparison results show that the model based on ResNet34 architecture has the best moisture content recognition effect, and the average error of moisture content prediction on the test set is about 2%. This model basically meets the requirement of real-time nondestructive monitoring of soil surface moisture content and can provide an essential means for the development of intelligent monitoring and construction technology in agricultural and geotechnical engineering.
soil moisture content / deep learning / convolutional neural network (CNN) / intelligent monitoring / intelligent construction / engineering geology
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Cai, Y., Zheng, W. G., Zhang, X., et al., 2019. Research on Soil Moisture Prediction Model Based on Deep Learning. PLoS One, 14(4): e0214508. https://doi.org/10.1371/journal.pone.0214508
|
|
Canziani, A., Paszke, A., Culurciello, E., 2016.An Analysis of Deep Neural Network Models for Practical Applications. ArXiv,abs/1605.07678. https://doi.org/10.48550/arXiv.1605.07678
|
|
Chang, D., Li, X., Liu, J.K., et al., 2014. Study Progress and Comparison of Soil Moisture Content Measurement Methods. Geotechnical Investigation & Surveying, 42(9): 17-22, 35 (in Chinese with English abstract).
|
|
Chen, T. Q., Moreau, T., Jiang, Z. H., et al., 2018. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. ArXiv, 1802.04799. http://arxiv.org/abs/1802.04799.pdf
|
|
Cheng, G.J., Li, B., Wan, X.L., et al., 2021. Research on Classification of Rock Section Image Based on Squeezenet Convolutional Neural Network. Mineralogy and Petrology, 41(4): 94-101 (in Chinese with English abstract).
|
|
Dawson, H. L., Dubrule, O., John, C. M., 2023. Impact of Dataset Size and Convolutional Neural Network Architecture on Transfer Learning for Carbonate Rock Classification. Computers & Geosciences, 171: 105284. https://doi.org/10.1016/j.cageo.2022.105284
|
|
Domínguez-Cuesta, M. J., Quintana, L., Valenzuela, P., et al., 2021. Evolution of a Human-Induced Mass Movement under the Influence of Rainfall and Soil Moisture. Landslides, 18(11): 3685-3693. https://doi.org/10.1007/s10346-021-01731-4
|
|
Fan, H. Y., Tian, Z. H., Xu, X. B., et al., 2022. Rockfill Material Segmentation and Gradation Calculation Based on Deep Learning. Case Studies in Construction Materials, 17: e01216. https://doi.org/10.1016/j.cscm.2022.e01216
|
|
Guo, T., Yu, H.B., 2018. Methods of Determination of Soil Water Content. Inner Mongolia Science Technology & Economy, (3): 66-67 (in Chinese with English abstract).
|
|
He, K. M., Zhang, X. Y., Ren, S. Q., et al., 2016. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, 770-778. https://doi.org/10.1109/CVPR.2016.90
|
|
Hou, X.L., Feng, Y.H., Wu, G.H., et al., 2016. Application Research on Artificial Neural Network Dynamic Prediction Model of Soil Moisture. Water Saving Irrigation, (7): 70-72, 76 (in Chinese with English abstract).
|
|
Huang,G.,Liu,Z.,Laurens,V.,et al.,2016. Densely Connected Convolutional Networks. 2017IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2261-2269. https://doi.org/10.1109/CVPR.2017.243
|
|
Huang, W.B., Ding, M.T., Wang, D., et al., 2022. Evaluation of Landslide Susceptibility Based on Layer Adaptive Weighted Convolutional Neural Network Model along Sichuan-Tibet Traffic Corridor. Earth Science, 47(6): 2015-2030 (in Chinese with English abstract).
|
|
Huang, X.H., Li, T.F., Liu, X.X., et al., 2022. Research on Tracking and Prediction of Rock Fissure Sevelopment Based on Improved Faster R-CNN Algorithm. Journal of Henan Polytechnic University (Natural Science), 41(4): 134-141 (in Chinese with English abstract).
|
|
Krizhevsky, A., Sutskever, I., Hinton, G. E., 2017. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6): 84-90. https://doi.org/10.1145/3065386
|
|
Lin, M., Chen, Q., Yan, S. C., 2014. Network in Network. ArXiv Preprint ArXiv, 1312.4400. https://doi.org/10.48550/arXiv.1312.4400
|
|
McBratney, A. B., Minasny, B., Viscarra Rossel, R., 2006. Spectral Soil Analysis and Inference Systems: A Powerful Combination for Solving the Soil Data Crisis. Geoderma, 136(1/2): 272-278. https://doi.org/10.1016/j.geoderma.2006.03.051
|
|
Ni, J.P., Gao, M., Wei, C.F., et al., 2009. Effects of Soil Water Content on Soil Shearing Strength to Different Soil Layer of Shallow Landslide. Journal of Soil and Water Conservation, 23(6): 48-50 (in Chinese with English abstract).
|
|
Persson, M., 2005. Estimating Surface Soil Moisture from Soil Color Using Image Analysis. Vadose Zone Journal, 4(4): 1119-1122. https://doi.org/10.2136/vzj2005.0023
|
|
Simonyan, K., Zisserman, A., 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. International Conference on Learning Representations (ICLR), San Diago, 1-14. https://doi.org/10.48550/arXiv.1409.1556
|
|
Song, B.H., Chen, W.W., Wu, W.J., et al., 2017. Experimental Study on the Dynamic Properties of Sliding Zone Soil of a Landslide under Varying Water Content. China Earthquake Engineering Journal, 39(4): 744-749, 758 (in Chinese with English abstract).
|
|
Sun, Y.L., Qu, Z.Y., Liu, Q.M., et al., 2020. Dynamic Study of Regional Soil Moisture Content Based on Multi-Source Remote Sensing Co-Inversion. Journal of Southwest University (Natural Science Edition), 42(12): 46-53 (in Chinese with English abstract).
|
|
Szegedy, C., Liu, W., Jia, Y., et al., 2015.Going Deeper with Convolutions. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Chicago, 1-9. https://doi.org/10.48550/arXiv.1409.4842
|
|
Tian, X., Daigle, H., 2018. Machine-Learning-Based Object Detection in Images for Reservoir Characterization: A Case Study of Fracture Detection in Shales. The Leading Edge, 37(6): 435-442. https://doi.org/10.1190/tle37060435.1
|
|
Wang, D.L., Shu, Y.G., 2017. Research Progress in Determination Methods for Soil Water Content. Journal of Mountain Agriculture and Biology, 36(2): 61-65 (in Chinese with English abstract).
|
|
Wang, M., Li, X., Chen, L. H., et al., 2020. A Modified Soil Water Content Measurement Technique Using Actively Heated Fiber Optic Sensor. Journal of Rock Mechanics and Geotechnical Engineering, 12(3): 608-619. https://doi.org/10.1016/j.jrmge.2019.11.003
|
|
Wang, Z.L., Wang, X.Y., Sun, L., 2012. Research of Variation Laws of Shear Strength between Invaded and Natural Collapsible Loess—A Case Study for Collapsible Loess in Gongyi of Henan. South-to-North Water Diversion and Water Science & Technology, 10(3): 123-126 (in Chinese with English abstract).
|
|
Yao, L., Wang, Y.R., Chen, Z., et al., 2020. Time-Varying Characteristics of Spatial Variability of Soil Moisture Content in Farmland. Water Saving Irrigation, (3): 33-39 (in Chinese with English abstract).
|
|
Yi, X. F., Li, H., Zhang, R. C., et al., 2022. Rock Mass Structural Surface Trace Extraction Based on Transfer Learning. Open Geosciences, 14(1): 98-110. https://doi.org/10.1515/geo-2022-0337
|
|
Yu, D.C., Ding, P., Yang, Z.Q., et al., 2021. Study on the Influence of Initial Water Content on the Breakout of Loess Landslide Barrier Dam. Mountain Research, 39(3): 367-377 (in Chinese with English abstract).
|
|
Zanetti, S. S., Cecílio, R. A., Alves, E. G., et al., 2015. Estimation of the Moisture Content of Tropical Soils Using Colour Images and Artificial Neural Networks. CATENA, 135: 100-106. https://doi.org/10.1016/j.catena.2015.07.015
|
|
Zhang, H., Wei, W.L., Liu, S.S., et al., 2023. Intelligent Identification Method of Moisture Content of Loess Based on Transfer Convolutional Neural Networks. Journal of Engineering Geology, 31(1): 21-31 (in Chinese with English abstract).
|
|
Zhao, N. Y., Jiang, Y., Song, Y., 2021. Recognition and Classification of Concrete Cracks under Strong Interference Based on Convolutional Neural Network. Traitement Du Signal, 38(3): 911-917. https://doi.org/10.18280/ts.380338
|
|
Zhao, Y.F., Wang, C.S., 2017. Principles and Comparison of Common Methods for Determining Soil Water Content. Horticulture & Seed, 37(10): 70-73 (in Chinese with English abstract).
|
|
Zheng, Z. W., Gao, Z. Z., 2020. Research on Influence of Soil Moisture Content of Farmland on Infiltration Model Parameters. E3S Web of Conferences, 189(3): 01011. https://doi.org/10.1051/e3sconf/202018901011
|
|
Zhou, C., Hu, G.L., Deng, L.Y., et al., 2021. Variation Characteristics of Soil Organic Matter and Water Content in Different Types of Landscape in the Middle Reaches of Heihe River. Journal of Gansu Agricultural University, 56(4): 126-135 (in Chinese with English abstract).
|
|
Zhou, S.G., 2007. Main Geology Disasters Type of Loess Tunnel. Geology and Prospecting, 43(2): 103-107 (in Chinese with English abstract).
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