Аннотации:
In this paper, a hybrid model based on RandomForestClassifier and MLPClassifier is presented, achieving an accuracy of
96.07% in the task of soil classification based on geophysical parameters. The results demonstrate the advantages of the
proposed approach over selected classical algorithms, indicating a high practical value for precision agriculture and
environmental monitoring. A dataset containing key soil parameters such as electrical conductivity, density, P-wave velocity,
and depth was utilized. Prior to training, the data were preprocessed: the target variable was converted to numeric format
using LabelEncoder, and the features were standardized using StandardScaler to bring them to a common scale. Data were
divided into training and test samples using the train_test_split method (80% training, 20% test).
