Dynamic Monitoring and Precision Fertilization Decision System for Agricultural Soil Nutrients Using UAV Remote Sensing and GIS

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Dades bibliogràfiques
Publicat a:	Agriculture vol. 15, no. 15 (2025), p. 1627-1654
Autor principal:	Chen, Xiaolong
Altres autors:	Zhang, Hongfeng, Wong Cora Un In
Publicat:	MDPI AG
Matèries:	Agricultural land Technological change Growth stage Crop yield Agricultural practices Recommender systems Unmanned aerial vehicles Fertilization Data integration Seasonal variations Machine learning Nitrogen Soil analysis Remote sensing Phosphorus Crop growth Nutrient status Decision making Remote sensing systems Geographic information systems Algorithms Environmental factors Real time Agricultural wastes Spatial analysis Accuracy Data processing Deep learning Agricultural production Soil testing Spectrum analysis Spatial discrimination Potassium Monitoring Soil nutrients Dynamic models Data collection Sustainable practices Agriculture Spectral analysis Nutrients Precision agriculture Sensors Soil conditions Sustainable agriculture Environmental
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Descripció
Resum:	We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices.
ISSN:	2077-0472
DOI:	10.3390/agriculture15151627
Font:	Agriculture Science Database