Soil Surface Texture Classification Using RGB Images

The classification of soil surface texture is critical for agricultural and environmental management, impacting properties like water-holding capacity and nutrient retention. Traditional methods relying on controlled environments face scalability challenges. This study introduces a scalable and high-resolution classification method using RGB images captured under uncontrolled field conditions (UFC). The proposed approach integrates image processing techniques, texture-enhancing filters, and Convolutional Neural Networks (CNNs) for accurate soil texture classification. The method involves image segmentation, tiling, application of Gabor filters, and CNN training, demonstrating improved performance over traditional techniques.