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: | , , |
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| Format: | article |
| Language: | Inglés |
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Frontiers
2024
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| 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 |
| Summary: | 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. |
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