Using Models and Artificial Neural Networks to Predict Soil Compaction Based on Textural Properties of Soils under Agriculture

This study focuses on addressing the challenges associated with labor-intensive soil penetration resistance (SPR) measurements, which are prone to errors due to varying soil moisture levels. The innovative approach involves developing SPR estimation models using artificial neural networks (ANN) for soils with optimal moisture levels determined by van Genuchten (WG) calculations. Sampling and measurements were conducted at 280 points (0–30 cm depth), with an additional 324 samples used for model testing. Considering six scenarios, this study aimed to identify the best estimation model using key soil properties (sand, clay, silt, bulk density, organic carbon, and aggregate stability) in different combinations affecting SPR. Results from all ANN scenarios demonstrated satisfactory SPR estimation performance, with the sand and clay content scenario exhibiting the highest accuracy, characterized by a mean square error (MSE) of 0.0029 and a coefficient of determination (R2) value of 0.9707. This selected scenario were further validated with different test data, yielding an MSE of 0.7891 and an R2 value of 0.67. In conclusion, this study suggests that, by standardizing moisture levels through WG calculations, ANN-based SPR estimation can effectively be applied to soils with specific sand and clay contents.