Soil Characterization and Classification: A Hybrid Approach of Computer Vision and Sensor Network

This paper presents soil characterization and classification using computer vision & sensor network approach. Gravity Analog Soil Moisture Sensor with arduino-uno and image processing is considered for classification and characterization of soils. For the data sets, Amhara regions and Addis Ababa city of Ethiopia are considered for this study. In this research paper the total of 6 group of soil and each having 90 images are used. That is, form these 540 images were captured. Once the dataset is collected, pre-processing and noise filtering steps are performed to achieve the goal of the study through MATLAB, 2013. Classification and characterization is performed through BPNN (Back-propagation neural network), the neural network consists of 7 inputs feature vectors and 6 neurons in its output layer to classify soils. 89.7% accuracy is achieved when back-propagation neural network (BPNN) is used

Application of Image Processing in Soil Mechanics

Whenever someone digs a hole in the ground or takes a relaxing walk on the beach, they directly interact with the soil. In its basic form, soil consists of solid, water, and air phases. Unlike water and metals, soil is a particulate media, and the relative proportion of the individual phases significantly influences its physical properties. Various studies have analyzed soil using image processing to determine its morphological properties. Research involving the digital processing of X-ray Computed Tomography (X-ray CT) images of soil to retrieve physically meaningful information is gaining recognition. This study investigates two-dimensional X-ray CT images of unsaturated granular media taken at specific locations on the wetting and drying curves of the soil water characteristic curve (SWCC) determined for the granular media. The images are segmented into three distinct phases of the media through image histogram-based thresholding. The porosity, void ratio, and degree of saturation are evaluated by counting the appropriate pixels for each phase and applying calibrations between pixels and physical dimensions

The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh