AI-Based Short-Term Precipitation Prediction in Precision Agriculture

Accurate short-term precipitation prediction with high spatial accuracy is important for optimizing agricultural practices in precision agriculture, where localized weather patterns can have a significant impact on crop management and resource allocation. Traditional forecasting methods often fall short in delivering the required precision, leading to inefficient irrigation, fertilization, and pesticide application. These inefficiencies result in significant economic losses and environmental damage. This study introduces the Predictive Error Compensated Neural Network model to enhance precipitation prediction by leveraging diverse meteorological data from Türkiye’s Agricultural Monitoring and Information System. The proposed model outperforms traditional models like Long Short-Term Memory and Prophet in predicting hourly precipitation rates and achieves $\mathbf{5 0 \%}$ fewer Mean Absolute Percentage Errors and three times fewer Root Mean Squared Errors and Mean Absolute Errors. The model supports better-informed agricultural decisions by providing more reliable precipitation forecasts to optimize resource management, improve crop yields, and promote both economic and environmental sustainability. This advancement holds the potential to significantly improve the resilience and efficiency of precision agriculture practices.