Search

• Smartphones show excellent ability to predict original soil properties. • Image segmentation significantly improves soil property prediction accuracy. • Significant differences in dominant regions between segmentation methods. • The K-means clustering segmentation method outperforms the OUST method. Prediction of soil properties based on images of original soil acquired from smartphones is considered a potential alternative to conventional soil property determination due to its convenience and low cost. However, images of the original soil may contain distracting information such as fissures, shadows, and rocks, which may affect the image feature extraction and property prediction results. Four unsupervised image segmentation methods, namely the maximum interclass variance (OUST) method, the local adaptive threshold segmentation (LATS) method, the K-means clustering segmentation (KMCS) method, and the natural break point segmentation (NBPS) method, were used to segment 62 images of original soil taken by smartphones in outdoor sunny conditions. The number of segmentation levels (N) for KMCS and NBPS varied from 2 to 10. The dominant region, determined as the region with the highest percentage of image area after segmentation, was selected for subsequent property prediction. The results of the segmentation methods revealed significant differences in the dominant regions, leading to significant variations in color and texture features (p < 0.05). The random forest approach was used to construct predictive models for soil Munsell colour, soil organic matter (SOM) content, and soil texture content. The validation results demonstrated that the image segmentation methods improved the prediction accuracy of all three soil properties compared to that of the unsegmented images. Among the segmentation methods, KMCS (N = 3 and 4) exhibited the highest segmentation performance, surpassing the widely used OUST method. This research highlights the significance of image segmentation methods in predicting soil properties based on images of original soil samples and recommended it as an essential process for future soil image processing. The findings of this study have the potential to advance the development of smartphone-based prediction of properties of original soils. [ABSTRACT FROM AUTHOR]