Continuous use of agricultural crop fields to achieve high yields leads to a decrease in the amount of nutrients and beneficial trace elements in the soil. To replenish the lost nutrients and trace elements in a short period of time, chemical fertilizers are additionally applied. Chemical fertilizers react with moisture and other substances in the soil, forming solid compounds that plants cannot absorb. By treating the soil with electrical impulse discharges, chemical fertilizers are converted into a form that can be absorbed by plants. In this study, the effect of soil electro-impulse treatment parameters on the content of trace elements was modeled using artificial neural networks. Based on the initial experimental data, a data augmentation method was applied, and a multi-output regression model was developed using a multilayer perceptron architecture. To evaluate the stability of the model, the K-Fold cross-validation method was used, and the average values of the coefficient of determination and mean squared error were calculated. The obtained results showed that the model operates with high predictive accuracy and can support near-real-time forecasting within a Digital Twin-oriented monitoring framework. The research results can be used to optimize electro-pulse technologies, automate agro-technological processes, and develop intelligent monitoring systems.
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