תמונות העטיפה

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

שמור ב:
הוצא לאור ב:Agriculture vol. 15, no. 15 (2025), p. 1627-1654
מחבר ראשי: Chen, Xiaolong
מחברים אחרים: Zhang, Hongfeng, Wong Cora Un In
יצא לאור:
MDPI AG
נושאים:
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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Resumen: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
Fuente:Agriculture Science Database