A multi-source information fusion method for soil compaction detection

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%.