Predicting bulk density and porosity of soil using image processing and support vector regression

Abstract: Soil structure is essential for plant development and moisture balance, generally representing the spatial heterogeneity of different components or properties of soil. In this case, bulk density and porosity of soil are important parameters to evaluate the soil structure. In the traditional measurement, the ring knife is normally used to measure soil bulk density. But this commonly-used measurement requires multiple instruments, such as ring cutters, aluminum boxes, and drying boxes, although the measured data is accurate to serve as a standard requirement. Particularly, the whole process is time-consuming and labor-intensive, unsuitable for the rapid and accurate measurement of soil bulk density in a large range of farmland in recent years. Therefore, it is highly urgent to explore a convenient, efficient, and indirect measurement of soil bulk density, especially for the input variables for most prediction models in precision agriculture. In this study, prediction models of soil bulk density and porosity were constructed with the soil resistance using image processing and Support Vector Regression (SVR). The color and texture parameters of the soil surface image were also used to characterize the soil roughness, according to the correlation between roughness, resistance, and bulk density of soil. A measuring system was developed to mount a vehicle for soil resistance. In image processing, HSV color space was used for the threshold segmentation, while the first-order distance, second-order moment, and third-order moment of HSV three components were taken as color parameters. The specific texture parameters included the energy, entropy, contrast, and inverse variance of the gray-level co-occurrence matrix. Principal component analysis was used to extract the principal components of color and texture parameters for the non-correlation between the input parameters. The correlation analysis was then made between the prediction of the SVR model and the standard value measured by the ring knife. Specifically, the coefficient of determination R2 of the SVR model reached 0.867 for the prediction of soil bulk density, the coefficient of determination R2 of decision tree regression model reached 0.734 for the prediction of soil bulk density, and the SVR model root mean square error was 0.001 g/cm3, indicating better performance than that of decision tree regression. Nevertheless, the calculation time took 6.810 s, about 4.7 s longer than the 2.153 s calculation time of decision tree regression. In soil porosity, the coefficient of determination R2 of the SVR model was 0.743, and the root mean square error was 2.284. The coefficient of determination R2 was 0.690 for the decision tree regression model, the root mean square error was 3.345. The calculation time of the SVR model was 3.144 s, less than the duration of the decision tree regression model at 4.302 s. It demonstrated that the SVR model can widely be expected to predict soil bulk density and porosity using color and texture parameters combined with soil resistance as input variables. In the case of small and medium-sized data samples, SVR model can achieve good prediction results. The finding can provide a sound reference for the rapid and effective prediction of bulk density and porosity in soil.