Soil water content (SWC) is a critical indicator for engineering construction, crop production, and the hydrologic cycle. The rapid and accurate assessment of SWC is of great importance. At present, digital images are becoming increasingly popular in environmental monitoring and soil property analysis owing to the advantages of non-destructiveness, cheapness, and high-efficiency. However, the capture of high-quality digital image and effective color information acquisition is challenging. For this reason, a photographic platform with an integrated experimental structure configuration was designed to yield high-quality soil images. The detrimental parameters of the platform including type and intensity of the light source and the camera shooting angle were determined after systematic exploration. A new method based on Gaussian fitting gray histogram for extracting RGB image feature parameters was proposed and validated. The correlation between 21 characteristic parameters of five color spaces (RGB, HLS, CIEXYZ, CIELAB, and CIELUV) and SWC was investigated. The model for the relationship between characteristic parameters and SWC was constructed by using least squares regression (LSR), stepwise regression (STR), and partial least squares regression (PLSR). Findings showed that the camera platform equipped with 45° illumination D65 light source, 90° shooting angle, 1900~2500 lx surface illumination, and operating at ambient temperature difference of 5 °C could produce highly reproducible and stable soil color information. The effects of image scale had a great influence on color feature extraction. The entire area of soil image, i.e., 3,000,000 pixels, was chosen in conjunction with a new method for obtaining color features, which is beneficial to eliminate the interference of uneven lightness and micro-topography of soil samples. For the five color spaces and related 21 characteristic parameters, RGB and CIEXYZ spaces and characteristic parameter of lightness both exhibited the strongest correlation with SWC. The PLSR model based on soil specimen images ID had an excellent predictive accuracy and the best stability (R2 = 0.999, RMSE = 0.236). This study showed the potential of the application of color information of digital images to predict SWC in agriculture and geotechnical engineering.
The influence of drying and wetting cycles on matric suction and volume characteristics of a compacted low plasticity clay soil was studied experimentally. An apparatus was developed where soil specimens were placed in direct contact with a high suction tensiometer, then repeated drying and wetting cycles were applied; drying by means of evaporation and wetting using the application of water droplets. The matric suction, vertical and radial displacement, and mass change of the specimens were all monitored continuously during the cycles. The equipment is the first to provide natural drying, unconstrained shrinkage or swelling with continuous measurements of volume, suction and water content in a way that could readily be used in engineering practice. The results indicated that drying-wetting cycles resulted in accumulated irreversible shrinkage. However, the amount of shrinkage decayed very significantly as the number of cycles increased, and the behaviour became almost repeatable after the third cycle. It was also observed that the positions of soil water retention curves (SWRC) under wetting-drying cycles shift downwards with the increasing number of cycles; the larger the number of cycles, the smaller the difference between the curves and after 2 or 3 cycles, the difference became steady. The shape of the curves changed very obviously under the first three wetting-drying cycles but less significantly after this.
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