One of the basic methods for soil analysis time and cost reduction is using soil sample spectral response in laboratory conditions. The problem with this method lies in determining the relationship between the shape of the soil spectral response and soil physical or chemical properties. The LUCAS soil database collected by the EU’s ESDAC research centre is good material to analyse the relationship between the soil properties and the near infrared (NIR) spectral response. The modelling described in the paper is based on these data. The analysis of the impact of soil properties configuration on absorbance levels in various NIR spectrum ranges was conducted using the stepwise regression models with the properties, properties squared and products of properties being explanatory variables. The analysis of partial correlation of soil properties values with absorbance values and absorbance derivative in the entire spectral range was conducted in order to evaluate the impact of the absorbance transformation (the first derivative of absorbance vector) on the change of significance of relationship with properties values. The Multi Layer Perceptron (MLP) models were used to estimate the absorbance relationship with single soil features. Soil property modelling based on the selection and transformation algorithm of raw values and first and second absorbance derivatives was also conducted along with the suitability evaluation of such models in building digital soil maps. The absorbance is affected by a limited number of tested soil features like pH, texture, content of carbonates, SOC, N, and CEC; P and K contents have, in case of this research, a negligible impact. The NIR methodology can be suitable in conditions of limited soil variation and particularly in development of thematic soil maps.
The aim of the study was to test the ability to model soil capability units diversity of on the basis of limited information about particle size and morphology of the terrain data. The data obtained from digitization of maps of agricultural soil and topography of the region of the Upper Silesian Industrial District. Rule extraction tools and build models were algorithms in the field of computational intelligence: different versions of decision trees, neural networks and deep learning algorithms. The best algorithms allow for correct classification to 90% of the elements of the validation set. The design ensemble of specialized classifier algorithm increased the efficiency of decision-making algorithm to identify a set of validation to about 94%. Proper selection decision algorithm allows the estimation of the likelihood vector belonging to a complex object. Computational intelligence algorithms can be considered as a tool for extracting classification rules from the collection of data on soils on the local or regional level.
The article presents the results of attempts to use adaptive algorithms for classification tasks different soils units. The area of study was the Upper Silesian Industrial Region, which physiographic and soils parameters in the form of digitized was used in the calculation. The study used algorithms, self-organizing map (SOM) of Kohonen, and classifiers: deep neural network, and two types of decision trees: Distributed Random Forest and Gradient Boosting Machine. Especially distributed algorithm Random Forest (algorithm DRF) showed a very high degree of generalization capabilities in modeling complex diversity of soil. The obtained results indicate, that the digitization of topographic and thematic maps give you a fairly good basis for creating useful models of soil classification. However, the results also showed that it cannot be concluded that the best algorithm presented in this research can be regarded as a general principle of system design inference.