Deep learning-based time series prediction for precision field crop protection

Tao He; Meijin Li; Dong Jin

doi:10.3389/fpls.2025.1575796

Deep learning-based time series prediction for precision field crop protection

Front Plant Sci. 2025 Jun 9:16:1575796. doi: 10.3389/fpls.2025.1575796. eCollection 2025.

Authors

Tao He¹, Meijin Li², Dong Jin³

Affiliations

¹ School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou, China.
² School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou, China.
³ Department of Art and Design, Taiyuan University, Shanxi, China.

Abstract

Introduction: Precision 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.

Methods: To 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.

Results: The 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.

Discussion: This research offers a transformative solution for precision agriculture, aligning with the pressing need for advanced tools in sustainable crop management.

Keywords: deep learning; precision agriculture; resource optimization; spatial-temporal modeling; time series prediction.