The Application of Spectral Techniques in Agriculture and Forestry
This Special Issue compiles 12 groundbreaking studies that advance spectral analysis technologies through interdisciplinary integration with machine learning. By synergizing remote sensing data and intelligent algorithms, the featured research establishes multidimensional monitoring frameworks spann...
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| Định dạng: | Online |
|---|---|
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Những chủ đề: | |
| Truy cập trực tuyến: | ONIX_20250812T110751_9783725836697_125 |
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| _version_ | 1869527752187576320 |
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| collection | Directory of Open Access Books |
| description | This Special Issue compiles 12 groundbreaking studies that advance spectral analysis technologies through interdisciplinary integration with machine learning. By synergizing remote sensing data and intelligent algorithms, the featured research establishes multidimensional monitoring frameworks spanning crop phenotyping to ecosystem assessment. Key advances include (1) novel vegetation indices derived from multi-angle hyperspectral data, resolving angular dependencies in leaf area index estimation, and enhanced convolutional neural networks, achieving 98.4% accuracy in maize disease detection under complex backgrounds; (2) L-band synthetic aperture radar innovations for vertical forest parameter retrieval and genetic-algorithm-optimized spectral models quantifying soil heavy-metal contamination via rice leaf signatures; and (3) spatiotemporal coupling of thermal infrared and multispectral techniques elucidating canopy temperature hysteresis mechanisms, enabling XGBoost-based crop water diagnostics for precision irrigation in arid regions. The studies, covering diverse agroforestry systems across European and Asian climates, validate spectral technology’s universal applicability to stress response monitoring, nutrient dynamics analysis, and sustainable farming practices. These innovations collectively construct an integrated "air–space–ground" intelligent sensing network, offering scalable solutions for food security under climate change. The findings mark spectral analysis’s transition into a data-driven decision-making era, establishing critical frameworks for real-time ecosystem monitoring and resource management. |
| format | Online |
| id | doab-20.500.12854ir-165369 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1653692025-08-12T09:25:13Z The Application of Spectral Techniques in Agriculture and Forestry Xiang, Youzhen precision agriculture unmanned aerial vehicles remote sensing plants thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries This Special Issue compiles 12 groundbreaking studies that advance spectral analysis technologies through interdisciplinary integration with machine learning. By synergizing remote sensing data and intelligent algorithms, the featured research establishes multidimensional monitoring frameworks spanning crop phenotyping to ecosystem assessment. Key advances include (1) novel vegetation indices derived from multi-angle hyperspectral data, resolving angular dependencies in leaf area index estimation, and enhanced convolutional neural networks, achieving 98.4% accuracy in maize disease detection under complex backgrounds; (2) L-band synthetic aperture radar innovations for vertical forest parameter retrieval and genetic-algorithm-optimized spectral models quantifying soil heavy-metal contamination via rice leaf signatures; and (3) spatiotemporal coupling of thermal infrared and multispectral techniques elucidating canopy temperature hysteresis mechanisms, enabling XGBoost-based crop water diagnostics for precision irrigation in arid regions. The studies, covering diverse agroforestry systems across European and Asian climates, validate spectral technology’s universal applicability to stress response monitoring, nutrient dynamics analysis, and sustainable farming practices. These innovations collectively construct an integrated "air–space–ground" intelligent sensing network, offering scalable solutions for food security under climate change. The findings mark spectral analysis’s transition into a data-driven decision-making era, establishing critical frameworks for real-time ecosystem monitoring and resource management. 2025-08-12T09:25:10Z 2025-08-12T09:25:10Z 2025 book ONIX_20250812T110751_9783725836697_125 9783725836697 9783725836703 https://directory.doabooks.org/handle/20.500.12854/165369 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10723 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3670-3 10.3390/books978-3-7258-3670-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725836697 9783725836703 236 open access |
| spellingShingle | precision agriculture unmanned aerial vehicles remote sensing plants thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries The Application of Spectral Techniques in Agriculture and Forestry |
| title | The Application of Spectral Techniques in Agriculture and Forestry |
| title_full | The Application of Spectral Techniques in Agriculture and Forestry |
| title_fullStr | The Application of Spectral Techniques in Agriculture and Forestry |
| title_full_unstemmed | The Application of Spectral Techniques in Agriculture and Forestry |
| title_short | The Application of Spectral Techniques in Agriculture and Forestry |
| title_sort | application of spectral techniques in agriculture and forestry |
| topic | precision agriculture unmanned aerial vehicles remote sensing plants thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | precision agriculture unmanned aerial vehicles remote sensing plants thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20250812T110751_9783725836697_125 |