Design of a soil texture analysis device based on ultrasound sensors and machine learning methods

In this thesis, a digital soil texture analysis system is designed and introduced, which can be an alternative to the traditional hydrometer method used to find the proportional distributions of sand, silt and clay minerals in the soil. Traditional methods have many disadvantages such as being completely mechanical, needing expert control and laboratory. Considering today's advanced technologies and innovations, it has become inevitable to design a computerized forecasting system. The system has been redesigned using a 3D-printed container with ultrasound sensors. The system makes predictions by interpreting the changes in the intensity of the sound signals passed through the soil-water mixture placed in a closed container with machine learning methods. The changes in these signals, which are obtained by utilizing the sedimentation properties of sand, silt and clay particles in the soil-water mixture at different rates, were recorded on the computer, and computerized estimation steps were applied to the data. By using Support Vector Regression and Multi-Layer Perceptron architectures, the success of machine learning methods have been compared against traditional hydrometer results of the sample soils. Considering the 10% margin of error accepted in the standard hydrometer method, it has been seen that the proposed machine learning supported automated texture analyzer produced acceptable results. Thus, a computerized soil texture analyzer, which can predict the percentages of sand, silt and clay in the soil-water mixture in a closed container, using machine learning methods, is independent of expert supervision and laboratory environment, has a high portability, and can work with less material, has been presented in detail.