Neural Network-Based Modeling for Precise Potato Yield Prediction Using Soil Parameters

This study analyses the potential of artificial neural networks (ANN) in accurately predicting potato yields based on 11 parameters characterising the soil environment. Accurate yield forecasting is crucial for optimising potato production, especially in the context of potato processing. Due to the significant impact of soil properties on yield, there is a need for comprehensive predictive models that take these factors into account. The field studies (2021–2024) included an analysis of soil parameters determining potato tuber yield. The developed ANN model was highly accurate, as evidenced by the following indicators: R2 = 0.8227, RMSE = 4.19 t∙ha−1, MAE = 3.35 t∙ha−1, MAPE = 7.34%. Global sensitivity analysis showed that cation exchange capacity (CEC), base saturation percentage (V), and sum of exchangeable bases (S) are key parameters influencing tuber yield. The results indicate that neural networks are effective in modelling complex relationships between soil parameters and potato yield, and that soil properties play a fundamental role in increasing yields and improving potato quality. The approach used may contribute to optimizing the nutrient content of potato tubers intended for French fry production. Future studies should incorporate climate data and micronutrients to enhance the accuracy of predictive models, potentially leading to a 10–15% improvement in yield predictions.