抄録
With the global shift of agriculture toward efficiency, sustainability, and eco-friendliness, precision fertilization and dynamic soil nutrient monitoring technologies are playing an increasingly vital role in improving crop yields and nutrient use efficiency while reducing environmental impacts. This paper systematically reviews the current applications and recent advances of big data technologies in precision agriculture, with a focus on the development of big data–based precision fertilization techniques and dynamic soil nutrient monitoring systems. First, the principles and limitations of traditional soil nutrient monitoring methods are reviewed, highlighting the advantages of data-driven monitoring approaches in terms of real-time performance, spatial resolution, and decision support. Then, the critical roles of sensor networks, remote sensing data integration, machine learning, and optimization models in soil nutrient monitoring and fertilization decision-making are discussed in depth, accompanied by case studies that demonstrate the practical outcomes and technical bottlenecks of precision fertilization systems in real-world agricultural production. Furthermore, the core challenges in integrating big data with precision fertilization—such as insufficient data standardization, limited model generalization, and inadequate economic feasibility and policy support—are summarized. Finally, future research directions and development suggestions are proposed from the perspectives of multi-source data fusion, intelligent decision algorithm optimization, and cross-scale system integration. This paper aims to provide theoretical support and practical guidance for promoting the innovative development of precision agriculture technologies and advancing sustainable agricultural production.
