Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks

被引：0
|
作者：
Pang Y. [1 ]
Wang Z. [1 ]
Li X. [1 ]
Du S. [1 ]
机构：
[1] School of Civil Engineering, Beijing Jiaotong University, Beijing
来源：
Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences | 2024年 / 49卷 / 05期
关键词：
convolutional neural network (CNN); deep learning; engineering geology; intelligent construction; intelligent monitoring; soil moisture content;
D O I：
10.3799/dqkx.2023.043
中图分类号：
学科分类号：
摘要：
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. © 2024 China University of Geosciences. All rights reserved.
引用
收藏
页码：1746 / 1758
页数：12
相关论文
共 37 条
  • [1] Cai Y., Zheng W. G., Zhang X., Et al., Research on Soil Moisture Prediction Model Based on Deep Learning, PLoS One, 14, 4, (2019)
  • [2] Canziani A., Paszke A., Culurciello E., An Analysis of Deep Neural Network Models for Practical Applications, (2016)
  • [3] Chang D., Li X., Liu J.K., Et al., Study Progress and Comparison of Soil Moisture Content Measurement Methods, Geotechnical Investigation & Surveying, 42, 9, pp. 17-22, (2014)
  • [4] Chen T. Q., Moreau T., Jiang Z. H., Et al., TVM: An Automated End-to-End Optimizing Compiler for Deep Learning, (2018)
  • [5] Cheng G.J., Li B., Wan X.L., Et al., Research on Classification of Rock Section Image Based on Squeezenet Convolutional Neural Network, Mineralo⁃ gy and Petrology, 41, 4, pp. 94-101, (2021)
  • [6] Dawson H. L., Dubrule O., John C. M., Impact of Dataset Size and Convolutional Neural Network Architecture on Transfer Learning for Carbonate Rock Classification, Computers & Geosciences, 171, (2023)
  • [7] Dominguez-Cuesta M. J., Quintana L., Valenzuela P., Et al., Evolution of a Human - Induced Mass Movement under the Influence of Rainfall and Soil Moisture, Landslides, 18, 11, pp. 3685-3693, (2021)
  • [8] Fan H. Y., Tian Z. H., Xu X. B., Et al., Rock-fill Material Segmentation and Gradation Calculation Based on Deep Learning, Case Studies in Construction Materials, 17, (2022)
  • [9] Guo T., Yu H. B., Methods of Determination of Soil Water Content, Inner Mongolia Science Technology & Economy, 3, pp. 66-67, (2018)
  • [10] He K. M., Zhang X. Y., Ren S. Q., Et al., Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, (2016)
1234