Editorial: Deep learning approaches applied to spectral images for plant phenotyping
Spectral Imaging, or imaging spectroscopy, is a widespread sensor technology used in precision agriculture, horticulture and plant phenotyping. From cameras providing just a few spectral bands on drones, to cameras with a large number of bands, often referred to as hyperspectral cameras on field...
| Main Authors: | , , |
|---|---|
| Format: | article |
| Language: | Inglés |
| Published: |
Frontiers
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.11939/8969 https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1425310/full |
| _version_ | 1855032887715299328 |
|---|---|
| author | Polder, Gerrit Blasco, José Cen, Haiyan |
| author_browse | Blasco, José Cen, Haiyan Polder, Gerrit |
| author_facet | Polder, Gerrit Blasco, José Cen, Haiyan |
| author_sort | Polder, Gerrit |
| collection | ReDivia |
| description | Spectral Imaging, or imaging spectroscopy, is a widespread sensor technology used in
precision agriculture, horticulture and plant phenotyping. From cameras providing just a
few spectral bands on drones, to cameras with a large number of bands, often referred to
as hyperspectral cameras on field vehicles or in greenhouses. For reasons outlined in
(Polder and Gowen, 2020), in this editorial paper, we employ the term “imaging
spectroscopy and spectral imaging”; however, within this Research Topic (RT), it is also
denoted as hyperspectral imaging. Imaging spectroscopy enables plant scientists to quantify
the composition of agricultural products, such as biomass, leaf area, and chlorophyll
content and also detect plant stresses and diseases in an early stage. |
| format | article |
| id | ReDivia8969 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Frontiers |
| publisherStr | Frontiers |
| record_format | dspace |
| spelling | ReDivia89692025-04-25T14:49:43Z Editorial: Deep learning approaches applied to spectral images for plant phenotyping Polder, Gerrit Blasco, José Cen, Haiyan Hyperspectral imaging Imaging spectroscopy Deep neural networks Convolutional neural networks Pre-trained networks N01 Agricultural engineering Multispectral imagery Spectral Imaging, or imaging spectroscopy, is a widespread sensor technology used in precision agriculture, horticulture and plant phenotyping. From cameras providing just a few spectral bands on drones, to cameras with a large number of bands, often referred to as hyperspectral cameras on field vehicles or in greenhouses. For reasons outlined in (Polder and Gowen, 2020), in this editorial paper, we employ the term “imaging spectroscopy and spectral imaging”; however, within this Research Topic (RT), it is also denoted as hyperspectral imaging. Imaging spectroscopy enables plant scientists to quantify the composition of agricultural products, such as biomass, leaf area, and chlorophyll content and also detect plant stresses and diseases in an early stage. 2024-09-03T07:49:26Z 2024-09-03T07:49:26Z 2024 article publishedVersion Polder, G., Blasco, J., & Cen, H. (2024). Deep learning approaches applied to spectral images for plant phenotyping. Frontiers in Plant Science, 15, 1425310. 1664-462X https://hdl.handle.net/20.500.11939/8969 10.3389/fpls.2024.1425310 https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1425310/full en Attribution-NonCommercial-NoDerivatives 4.0 Internacional Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ openAccess Frontiers electronico |
| spellingShingle | Hyperspectral imaging Imaging spectroscopy Deep neural networks Convolutional neural networks Pre-trained networks N01 Agricultural engineering Multispectral imagery Polder, Gerrit Blasco, José Cen, Haiyan Editorial: Deep learning approaches applied to spectral images for plant phenotyping |
| title | Editorial: Deep learning approaches applied to spectral images for plant phenotyping |
| title_full | Editorial: Deep learning approaches applied to spectral images for plant phenotyping |
| title_fullStr | Editorial: Deep learning approaches applied to spectral images for plant phenotyping |
| title_full_unstemmed | Editorial: Deep learning approaches applied to spectral images for plant phenotyping |
| title_short | Editorial: Deep learning approaches applied to spectral images for plant phenotyping |
| title_sort | editorial deep learning approaches applied to spectral images for plant phenotyping |
| topic | Hyperspectral imaging Imaging spectroscopy Deep neural networks Convolutional neural networks Pre-trained networks N01 Agricultural engineering Multispectral imagery |
| url | https://hdl.handle.net/20.500.11939/8969 https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1425310/full |
| work_keys_str_mv | AT poldergerrit editorialdeeplearningapproachesappliedtospectralimagesforplantphenotyping AT blascojose editorialdeeplearningapproachesappliedtospectralimagesforplantphenotyping AT cenhaiyan editorialdeeplearningapproachesappliedtospectralimagesforplantphenotyping |