A Comprehensive study on the different types of soil desiccation cracks and their implications for soil identification using deep learning techniques.

Abstract

Rapid drying of soil leads to its fracture. The cracks left behind by these fractures are best seen in soils such as clays that are fine in the texture and shrink on drying, but this can be seen in other soils too. Hence, different soils from the same region show different characteristic desiccation cracks and can thus be used to identify the soil type. In this paper, three types soils namely clay, silt, and sandy-clay-loam from the Brahmaputra river basin in India are studied for their crack patterns using both conventional studies of hierarchical crack patterns using Euler numbers and fractal dimensions, as well as by applying deep-learning techniques to the images. Fractal dimension analysis is found to be an useful pre-processing tool for deep learning image analysis. Feed forward neural networks with and without data augmentation and with the use of filters and noise suggest that data augmentation increases the robustness and improves the accuracy of the model. Even on the introduction of noise, to mimic a real-life situation, 92.09% accuracy in identification of soil was achieved, proving the combination of conventional studies of desiccation crack images with deep learning algorithms to be an effective tool for identification of real soil types.