A Hybrid Ensemble Framework for Smart Irrigation: Optimizing Water Management in Precision Agriculture

Efficient water management is a critical challenge in precision agriculture, where traditional irrigation systems often rely on static schedules or conventional machine learning models with limited adaptability. These approaches struggle to balance accuracy, efficiency, and scalability, leading to suboptimal water distribution and resource usage. This research addresses these limitations by proposing an ensemble-based intelligent irrigation framework that integrates stacking models and a novel voting layer to enhance predictive accuracy, adaptability, and scalability. The methodology involves combining standalone machine learning algorithms, including Decision Tree, Random Forest, XGBoost, and Gradient Boost, with ensemble techniques such as stacking and voting. Additionally, a Long Short-Term Memory (LSTM) model is implemented to determine irrigation levels based on different crop types, providing further verification of the irrigation predictions. The framework prioritizes critical features like soil moisture, crop type, and growth stage to predict irrigation needs dynamically. Experimental evaluation on the Crop Irrigation Scheduling dataset demonstrates the model's effectiveness, achieving ۹۹% accuracy—significantly outperforming other stand alone (best=۹۶%) and ensemble approaches (best=۹۵%). Compared to existing research and algorithms, the proposed model offers a transformative shift from static to adaptive irrigation strategies, providing a scalable and efficient solution for sustainable water management in agriculture.

کلیدواژه ها:

Smart irrigation ، Precision agriculture ، Hybrid Model ، Ensemble Methods ، Irrigation level