In this study, an approach is presented that compares curve fitting, support vector regression, multilayer perceptron, and long short-term memory architecture to reduce the experiment duration in the formerly proposed Ultrasound Penetration-Based Digital Soil Texture Analyzer (USTA) device, which can automatically, affordably, and effortlessly determine soil texture analysis. The primary objective is to minimize the standard 2-hour experiment time while maintaining an acceptable level of accuracy. To achieve this, signals comprising 14400 samples collected from 52 soil specimens within a 2-hour time-frame using the USTA device were utilized. First, many short variations of the signal were created by either trimming 500 samples at a time from the end of each signal or adding 500 samples from the beginning of each signal. Deviation values were then estimated by comparing these variations to the original signals using different methods. Subsequently, by comparing error values, the best shortened variation was determined. In the curve fitting method, second-degree exponential equations were selected as the best-fit curves using the R-squared method. After extensive fine-tuning and experimentation with various methods, it was found that the best results for reducing experiment duration were achieved using Long Short-Term Memory.
Albayrak et al. (Sun,) studied this question.