College of Resource and Environment,Southwest University
National Natural Science Foundation of China (No. 41977002) and the Fundamental Research Funds for the Central Universities（No. XDJK2020B069）
【Objective】Pedology begins with the observation of soil profile and its morphological characteristics. The division of the soil profile horizon and description of the characteristics of the horizon boundary are the basis of soil investigation. The division of soil horizon in the field requires rich practical experience in pedology and is more subjective, which makes it difficult to form a set of unified division standards. 【Method】In this paper, the purple soil profile image was taken as the research object, and using K-means clustering and image segmentation technology, combined with the color (CIE Lab color space) and texture characteristics (Entropy) of the image, we identified the horizon boundary of the purple soil profile, by comparing with the results of field division. 【Result】The results show that (1) the a and b channels of CIE Lab color space and Entropy texture characteristics can delineate the master horizon (A , B , and C) and bedrock (R) of the profile; the a channel values range from 7 - 22, the b channel values range from 7 -19, and the Entropy values were 4 or 5; the Munsell colors converted by the CEL XYZ system had a certain deviation from the colors visually discerned in the field using colorimetric cards, with a hue range of 10R- 2.5Y, a value range of 4 - 8, and a chroma range of 3 - 8. (2) The number of soil horizon and the depth of soil horizon identified by clustering were consistent with the results of field identification; the difference between the lower boundary depth of soil horizon identified by clustering identification and those identified in the field was within 3 cm, except for C in profile Z2 and the Ap in profile Z6, where the difference was larger (13 cm and 8 cm, respectively). (3) The topography of the soil horizon identified by clustering was more irregular and the distinctness was more blurred. The clustering algorithm can identify more subtle differences in the soil profile image and reflect the local variation of soil properties in more detail. 【Conclusion】K-means clustering and image segmentation techniques achieved the identification of the horizon boundary of purple soil, and this study provides a scientific reference for the development of an intelligent identification system for soil profiles.