Systematic Dataset Generation for Soil Texture Classification Based on the USDA Soil Classification Triangle

This study introduces a novel soil texture dataset designed to overcome geographic constraints and improve the generalization of classification models. Using the USDA soil classification triangle as a framework, the dataset is systematically generated by combining pure sand, silt, and clay in varying proportions to create diverse soil texture classes. The soil mixtures are captured using a multispectral sensor with seven bands, ensuring a rich representation of spectral information. This self-generated dataset enables the development and evaluation of advanced classification techniques, offering a standardized and comprehensive resource for soil texture studies. By addressing the limitations of existing datasets, this work provides a robust foundation for advancing soil texture classification research across diverse fields.