A new autonomous program customized for computing surface cracks in an unsaturated soil in a 1-D column

The quantification of soil surface cracks is important, as it is useful in analyzing water infiltration and overall water balance in any green infrastructure, such as slopes, agricultural fields, green roofs, etc. In previously reported studies, the approaches for quantifying cracks mainly used manual processing of images through the public domain image analysis tool ImageJ. Such software is not customized for quantifying cracks in an unsaturated soil surface, as this results in relatively higher noise (i.e., lower resolution) in the processed image. Furthermore, manual processing makes processing of images in large quantities (usually captured through unmanned aerial vehicle (UAV) surveying) cumbersome. This technical note introduces an autonomous novel image analysis method for characterizing surface crack patterns that develop in unsaturated soils. A simple experimental setup was developed using a 1-D column containing red soil. The soil was compacted by hand to the desired state of compaction and placed in an environment-controlled chamber where it was allowed to dry. A series of images of the soil sample was captured using a commercially available camera model (Canon EOS 700D) to have photographic representation of the cracking process. A step-by-step strategy using a script coded in Python was developed to analyze the images captured during the laboratory tests. It outlines how image analysis can be automated to remove observer-dependent subjectivity (involved in manual processing of images) and introduces reproducibility of results. In addition, it effectively quantifies cracks in unsaturated soils with a much lower processing time and higher accuracy (less noise).