Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction

作者：Hang Xing, Zeyang Zhong, Wenhao Zhang, Yu Jiang, Xinyu Jiang, Xiuli Yang, Weizi Cai, Shuanglong Wu, Long Qi · 发表于：Sensors · 年份：2025 · DOI：10.3390/s25103223 · 被引用次数：1 · 研究领域：Smart Agriculture and AI、Soil erosion and sediment transport、Soil Mechanics and Vehicle Dynamics

Soil porosity, as an essential indicator for assessing soil quality, plays a key role in guiding agricultural production, so it is beneficial to detect soil porosity. However, the currently available methods do not apply to high-precision and rapid detection of soil with a black-box nature in the field, so this paper proposes a soil porosity detection method based on ultrasound and multi-scale CNN-LSTM. Firstly, a series of ring cutter soil samples with different porosities were prepared manually to simulate soil collected in the field using a ring cutter, followed by ultrasonic signal acquisition of the soil samples. The acquired signals were subjected to three kinds of data augmentation processes to enrich the dataset: adding Gaussian white noise, time shift transformation, and random perturbation. Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. The multi-head self-attention mechanism is added at the end of the model to infer the before-and-afte...