LGB tabanli toprak tekstür anali̇zleri̇nde test süresi̇ni̇n kisaltilmasi

In this thesis, an approach based on curve fitting, support vector regression, multilayer perceptron and long short-term memory architecture is proposed to shorten the experiment time in experiments with the Laser Guided Bouyoucos device developed for soil texture analysis. For this purpose, texture analysis signals obtained from 52 soil samples, each with 14400 samples (2 hours), were used. In the traditionally used curve fitting method, the shortest signal segment is found with an acceptable absolute error by shortening the soil texture signals from the end, while in machine learning methods, the shortest signal segment is found starting from the beginning. In the curve fitting method, the most suitable curve for the soil signals was selected as the 2nd degree exponential equation with the R-squared method. In the time shortening study with SVR, the model was trained and tested in the sample range of 1000-7000. In order to determine the entrance segment length in the MLP method, tests were carried out with a segment size in the range of 50-950 and the entrance segment size was selected as 200. In the MLP method, 3 layers are used: 1 input layer with 200 inputs, 1 hidden layer with 100 neurons, and 1 output layer with output. In the LSTM method, a 3-layer architecture is used, including the 1-input input layer, the 200-neuron LSTM layer, and the single-output output layer. As a result of the time shortening studies, the soil components were estimated with the SVR.