A multi-source information fusion method for soil compaction detection

Yuting Hu; Ming Fang; Song Gao

doi:10.1117/12.2675232

25 May 2023 A multi-source information fusion method for soil compaction detection

Yuting Hu, Ming Fang, Song Gao

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263630 (2023) https://doi.org/10.1117/12.2675232
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In this study, from the soil compaction characteristics, the Swin Transformer network in deep learning method is used to identify the soil surface rough and smooth, and compared with the traditional network method, proving that the algorithm in this paper has good results in terms of recognition accuracy and recognition speed; using Mask leveling, wavelet transform denoising and other steps in the traditional image processing method to identify whether the soil contains cracking; using the methods of physics, mathematics and soil science, fusing gravity sensors to detect soil water content, porosity, etc; using BP neural network, fusing the extracted features to achieve the purpose of real-time detection of soil compaction, and the experimental results show that the accuracy of this algorithm is 98.8%.

Citation Download Citation

Yuting Hu, Ming Fang, and Song Gao "A multi-source information fusion method for soil compaction detection", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263630 (25 May 2023); https://doi.org/10.1117/12.2675232

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KEYWORDS

Soil science

Porosity

Detection and tracking algorithms

Information fusion

Neural networks

Soil moisture

Image segmentation

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