A neighborhood median weighted fuzzy c-means method for soil pore identification

Complex soil pore geometry and heterogeneity determines the ability of a soil to retain moisture and conduction. These soil properties are widely recognized as key factors of essential ecological functions and services. However, until recently, the existing pore identification methods have the problems of low identification accuracy and operating efficiency, which has restricted the development of soil science. The objective of this study was to propose a neighborhood median weighted fuzzy c-means method based on grayscale-gradient feature (NMFCM-G) to identify soil pore structure automatically and accurately. By combining three-dimensional (3D) printing technology with X-ray computed tomography technology, a 3D simulation model with known aperture (10-cm inner diameter) was adopted to evaluate the pore identification error rate of the NMFCM-G method quantitatively. Compared to the methods commonly applied in previous studies, the NMFCM-G method had the smallest average area relative error (2.98%), which was only 1/6 of that of the Image J method with the largest area relative error (18.46%). The NMFCM-G method also had the smallest average perimeter (5.46%), about 3/5 of that of the Image-Pro Plus method with the largest perimeter relative error (8.35%). Meanwhile, the NMFCM-G method was successfully tested on undisturbed cylindrical soil samples, providing encouraging results in terms of identifying irregular pore structure from the complex hierarchical organization of soil. The results show that the NMFCM-G method had the smallest distribution entropy (0.81), the smallest inter-class correlation (0.164 0), and the largest distribution coefficient (0.11), which proves that the NMFCM-G method performed the best in identifying different soil pore structures. Overall, the NMFCM-G method provides new insights into identifying pore structures and thus could provide an automatic and high-efficiency technique for studying the effects of tillage and freeze-thaw cycles on pore structure and soil quality in the future.

Automated summary

This study proposes a neighborhood median weighted fuzzy c-means method based on grayscale-gradient feature (NMFCM-G) to automatically and accurately identify soil pore structure. The method demonstrated high accuracy and efficiency in pore identification, providing a new technique for studying the effects of tillage and freeze-thaw cycles on pore structure and soil quality.