Dynamic Monitoring and Precision Fertilization Decision System for Agricultural Soil Nutrients Using UAV Remote Sensing and GIS
محفوظ في:
| الحاوية / القاعدة: | Agriculture vol. 15, no. 15 (2025), p. 1627-1654 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , |
| منشور في: |
MDPI AG
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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MARC
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| 022 | |a 2077-0472 | ||
| 024 | 7 | |a 10.3390/agriculture15151627 |2 doi | |
| 035 | |a 3239015499 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231331 |2 nlm | ||
| 100 | 1 | |a Chen, Xiaolong | |
| 245 | 1 | |a Dynamic Monitoring and Precision Fertilization Decision System for Agricultural Soil Nutrients Using UAV Remote Sensing and GIS | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Agricultural land | ||
| 653 | |a Technological change | ||
| 653 | |a Growth stage | ||
| 653 | |a Crop yield | ||
| 653 | |a Agricultural practices | ||
| 653 | |a Recommender systems | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Fertilization | ||
| 653 | |a Data integration | ||
| 653 | |a Seasonal variations | ||
| 653 | |a Machine learning | ||
| 653 | |a Nitrogen | ||
| 653 | |a Soil analysis | ||
| 653 | |a Remote sensing | ||
| 653 | |a Phosphorus | ||
| 653 | |a Crop growth | ||
| 653 | |a Nutrient status | ||
| 653 | |a Decision making | ||
| 653 | |a Remote sensing systems | ||
| 653 | |a Geographic information systems | ||
| 653 | |a Algorithms | ||
| 653 | |a Environmental factors | ||
| 653 | |a Real time | ||
| 653 | |a Agricultural wastes | ||
| 653 | |a Spatial analysis | ||
| 653 | |a Accuracy | ||
| 653 | |a Data processing | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Soil testing | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Spatial discrimination | ||
| 653 | |a Potassium | ||
| 653 | |a Monitoring | ||
| 653 | |a Soil nutrients | ||
| 653 | |a Dynamic models | ||
| 653 | |a Data collection | ||
| 653 | |a Sustainable practices | ||
| 653 | |a Agriculture | ||
| 653 | |a Spectral analysis | ||
| 653 | |a Nutrients | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Sensors | ||
| 653 | |a Soil conditions | ||
| 653 | |a Sustainable agriculture | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhang, Hongfeng | |
| 700 | 1 | |a Wong Cora Un In | |
| 773 | 0 | |t Agriculture |g vol. 15, no. 15 (2025), p. 1627-1654 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3239015499/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3239015499/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3239015499/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |