ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1575796

This article is part of the Research TopicAdvanced Methods, Equipment and Platforms in Precision Field Crops Protection, Volume IIView all 15 articles

Deep Learning-Based Time Series Prediction for Precision Field Crop Protection

Provisionally accepted
  • Taiyuan University, Taiyuan, China

The final, formatted version of the article will be published soon.

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 spatialtemporal dependencies. These limitations hinder their ability to provide actionable insights for resource optimization and environmental protection. In order 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. 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. This research offers a transformative solution for precision agriculture, aligning with the pressing need for advanced tools in sustainable crop management.

Keywords: precision agriculture, time series prediction, deep learning, Resource optimization, Spatial-temporal modeling

Received: 13 Feb 2025; Accepted: 06 May 2025.

Copyright: © 2025 Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jin Dong, Taiyuan University, Taiyuan, China

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