Real-Time Paddy Field Irrigation Using Feature Extraction and Federated Learning Strategy

Paddy field irrigation is crucial for high crop yields and food security, yet traditional methods lack precision and adaptability to changing environmental conditions. Existing research is hindered by biased public datasets, inadequate feature extraction, and centralized processing that obstructs real-time decision-making. To address these challenges, this work develops a comprehensive testbed for collecting diverse sensor data from paddy fields under various weather conditions and seasons. We propose a novel hybrid and ensemble feature extraction (HyEn-X) method to enhance data quality and predictive accuracy. In addition, we incorporate federated learning (FL) with hyperparameter tuning and explainable AI (XAI) to validate and optimize the proposed feature extraction approach. This methodology not only reduces noise and irrelevant features but also ensures real-time, localized decision-making for farmers. The proposed methodology improves prediction accuracy, accelerates model convergence, and reduces communication overhead. Furthermore, we have developed a hardware prototype that farmers can use to receive real-time irrigation recommendations. Experimental results demonstrate that the proposed method significantly outperforms baseline feature extraction techniques and validates its effectiveness in practical agricultural settings.