Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture
When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies. Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent resea...
Furkejuvvon:
| Materiálatiipa: | Online |
|---|---|
| Giella: | eaŋgalasgiella |
| Almmustuhtton: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
|
| Fáttát: | |
| Liŋkkat: | ONIX_20230307_9783036566146_48 |
| Fáddágilkorat: |
Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!
|
| _version_ | 1869529612752519168 |
|---|---|
| collection | Directory of Open Access Books |
| description | When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies. Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent research in precision agriculture has used multispectral images from different platforms, such as satellites, airborne, and, most recently, drones. These images have been used for various analyses, from the detection of pests and diseases, growth, and water status of crops to yield estimations. However, accurately detecting specific biotic or abiotic stresses requires a narrow range of spectral information to be analyzed for each application. In data analysis, the volume and complexity of data formats obtained using the latest technologies in remote sensing (e.g., a cube of data for hyperspectral imagery) demands complex data processing systems and data analysis using multiple inputs to estimate specific categorical or numerical targets. New and emerging methodologies within artificial intelligence, such as machine learning and deep learning, have enabled us to deal with these increasing data volumes and the analysis complexity. |
| format | Online |
| id | doab-20.500.12854ir-98038 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-980382024-03-31T13:10:08Z Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture Chang, Jiyul Fuentes, Sigfredo vineyard pesticide application variable rate application unmanned aerial vehicle satellite nanosatellite monsoon crops leaf area index leaf chlorophyll concentration crop water content multispectral hyperspectral deep learning forage dry matter yield high-throughput phenotyping Brazilian pasture nitrogen indicator nitrogen nutrition diagnosis optical sensor spectral index UAV wheat lodging lightweight digital surface model (DSM) winter wheat fractional order differential continuous wavelet transform optimal subset regression support vector machine wheat powdery mildew machine learning information fusion remote sensing monitoring hyperspectral imaging dimensionality reduction LDA PLS PCA RandomForest ReliefF XGB Meloidogyne Solanum tuberosum soil salinity sensitive parameter random forest optimal retrieval model remote sensing high throughput phenotyping UAV/drone biomass estimation oats wheat yield prediction random forests satellite imagery Normalized Difference Vegetation Index (NDVI) n/a thema EDItEUR::M Medicine and Nursing When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies. Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent research in precision agriculture has used multispectral images from different platforms, such as satellites, airborne, and, most recently, drones. These images have been used for various analyses, from the detection of pests and diseases, growth, and water status of crops to yield estimations. However, accurately detecting specific biotic or abiotic stresses requires a narrow range of spectral information to be analyzed for each application. In data analysis, the volume and complexity of data formats obtained using the latest technologies in remote sensing (e.g., a cube of data for hyperspectral imagery) demands complex data processing systems and data analysis using multiple inputs to estimate specific categorical or numerical targets. New and emerging methodologies within artificial intelligence, such as machine learning and deep learning, have enabled us to deal with these increasing data volumes and the analysis complexity. 2023-03-07T16:30:31Z 2023-03-07T16:30:31Z 2023 book ONIX_20230307_9783036566146_48 9783036566146 9783036566153 https://directory.doabooks.org/handle/20.500.12854/98038 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6765 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6615-3 10.3390/books978-3-0365-6615-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036566146 9783036566153 226 Basel open access |
| spellingShingle | vineyard pesticide application variable rate application unmanned aerial vehicle satellite nanosatellite monsoon crops leaf area index leaf chlorophyll concentration crop water content multispectral hyperspectral deep learning forage dry matter yield high-throughput phenotyping Brazilian pasture nitrogen indicator nitrogen nutrition diagnosis optical sensor spectral index UAV wheat lodging lightweight digital surface model (DSM) winter wheat fractional order differential continuous wavelet transform optimal subset regression support vector machine wheat powdery mildew machine learning information fusion remote sensing monitoring hyperspectral imaging dimensionality reduction LDA PLS PCA RandomForest ReliefF XGB Meloidogyne Solanum tuberosum soil salinity sensitive parameter random forest optimal retrieval model remote sensing high throughput phenotyping UAV/drone biomass estimation oats wheat yield prediction random forests satellite imagery Normalized Difference Vegetation Index (NDVI) n/a thema EDItEUR::M Medicine and Nursing Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture |
| title | Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture |
| title_full | Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture |
| title_fullStr | Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture |
| title_full_unstemmed | Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture |
| title_short | Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture |
| title_sort | methodologies used in remote sensing data analysis and remote sensors for precision agriculture |
| topic | vineyard pesticide application variable rate application unmanned aerial vehicle satellite nanosatellite monsoon crops leaf area index leaf chlorophyll concentration crop water content multispectral hyperspectral deep learning forage dry matter yield high-throughput phenotyping Brazilian pasture nitrogen indicator nitrogen nutrition diagnosis optical sensor spectral index UAV wheat lodging lightweight digital surface model (DSM) winter wheat fractional order differential continuous wavelet transform optimal subset regression support vector machine wheat powdery mildew machine learning information fusion remote sensing monitoring hyperspectral imaging dimensionality reduction LDA PLS PCA RandomForest ReliefF XGB Meloidogyne Solanum tuberosum soil salinity sensitive parameter random forest optimal retrieval model remote sensing high throughput phenotyping UAV/drone biomass estimation oats wheat yield prediction random forests satellite imagery Normalized Difference Vegetation Index (NDVI) n/a thema EDItEUR::M Medicine and Nursing |
| topic_facet | vineyard pesticide application variable rate application unmanned aerial vehicle satellite nanosatellite monsoon crops leaf area index leaf chlorophyll concentration crop water content multispectral hyperspectral deep learning forage dry matter yield high-throughput phenotyping Brazilian pasture nitrogen indicator nitrogen nutrition diagnosis optical sensor spectral index UAV wheat lodging lightweight digital surface model (DSM) winter wheat fractional order differential continuous wavelet transform optimal subset regression support vector machine wheat powdery mildew machine learning information fusion remote sensing monitoring hyperspectral imaging dimensionality reduction LDA PLS PCA RandomForest ReliefF XGB Meloidogyne Solanum tuberosum soil salinity sensitive parameter random forest optimal retrieval model remote sensing high throughput phenotyping UAV/drone biomass estimation oats wheat yield prediction random forests satellite imagery Normalized Difference Vegetation Index (NDVI) n/a thema EDItEUR::M Medicine and Nursing |
| url | ONIX_20230307_9783036566146_48 |