Deep learning-based time series prediction for precision field crop protection - He, Tao; Li, Meijin; Jin, Dong (Frontiers in Plant Science, 2025)
IntroductionPrecision agriculture is revolutionizing modern farming by integrating data-driven methodologies to enhance crop productivity while promoting sustainability. Traditional time series models struggle with complex agricultural data due to heterogeneity, high dimensionality, and strong spatial-temporal dependencies. These limitations hinder their ability to provide actionable insights for resource optimization and environmental protection.MethodsTo tackle these difficulties, this research puts forward a new deep-learning-based architecture for time-series prediction that is customized for precise field crop protection. At its core, our Spatially-Aware Data Fusion Network (SADF-Net) integrates multi-modal data sources, such as satellite imagery, IoT sensor readings, and meteorological forecasts, into a unified predictive model. By combining convolutional layers for spatial feature extraction, recurrent neural networks for temporal modeling, and attention mechanisms for data fusion, SADF-Net captures intricate spatial-temporal dependencies while ensuring robustness to noisy and incomplete data. We introduce the Resource-Aware Adaptive Decision Algorithm (RAADA), which leverages reinforcement learning to translate SADF-Net’s predictions into optimized strategies for resource allocation, such as irrigation scheduling and pest control. RAADA dynamically adapts decisions based on real-time field responses, ensuring efficiency and sustainability.ResultsThe experimental findings obtained from large-scale agricultural datasets show that our framework far exceeds the existing most advanced methods in terms of the accuracy of yield prediction, resource optimization, and environmental impact mitigation.DiscussionThis research offers a transformative solution for precision agriculture, aligning with the pressing need for advanced tools in sustainable crop management.