Toward Flexible Soil Texture Detection by Exploiting Deep Spectrum and Texture Coding

Soil texture is a significant attribute of soil properties. Obtaining insight into the soil texture is beneficial when making agricultural decisions during production. Nevertheless, assessing the soil texture in specific laboratory conditions entails substantial dedication, which is time-consuming and includes a high cost. In this paper, we propose a soil texture detection network by embedding the frequency channel attention network and a texture encoding network into the representation learning paradigm of the ResNet framework. Concretely, the former is reliable in exploiting the feature correlations among multi-frequency, while the latter focuses on encoding feature variables, jointly enhancing the ability of feature expression. Meanwhile, the clay, silt, and sand particles present in the soil are exported through a ResNet18 fully linked layer. Experimental results show that the correlation coefficient for predicting clay, silt, and sand content are 0.931, 0.936, and 0.957, respectively. For the root mean square error, the quantitative scores are 2.106%, 3.390%, and 3.602%, respectively. The proposed network also exhibits proposing generalization capability, yielding quite considerable results on different soil samples. Notably, the detection results are almost in agreement with the conventional laboratory measurements, and, at the same time, outperform other competitors, making it highly attractive for practical applications.