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« On-the-go » multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices
- Source :
- Precision Agriculture, Precision Agriculture, Springer Verlag, 2016, 18 (3), pp.293-308. 〈10.1007/s11119-016-9489-y〉, Precision Agriculture, Springer Verlag, 2016, 18 (3), pp.293-308. ⟨10.1007/s11119-016-9489-y⟩
- Publication Year :
- 2016
- Publisher :
- HAL CCSD, 2016.
-
Abstract
- EA GESTAD Agrosup CT1; International audience; Over the last years, the literature presents new technologies to optimize vineyard management. In the proximal sensing context, optical sensors are mainly developed to characterize the vegetation and the most famous one is the Greenseeker RT-100 (Trimble, Germany), that provides NDVI. The interpretation of its measurements is complex because it overlaps quantitative and qualitative information. However, it is a robust active sensor especially dedicated to characterize vineyard at early growth stage. To overcome these limits, we developed a multispectral (RGB, NIR) imaging system. We present a first application of spectral imagery, in proximal sensing conditions, to characterize the vine foliage of three grapevine varieties (Meunier, Pinot Noir and Chardonnay) at four phenological stages. The imaging system is embedded on a ground vehicle acquiring images with natural light, and an original radiometric calibration is proposed. From images, three agronomic indices (NDVIimage, NDVIvegetation and “foliage occupation”) are defined. They are computed from entire images and from the area of the grapes. These indices are compared to Greenseeker ones at the beginning of berry formation to be assessed. Whatever the grapevine variety the NDVIimage is in agreement with the index provided by Greenseeker (NDVIGS). At the other stages, the comparison of NDVIGS to the other indices leads to a new interpretation of NDVIGS depending on the phenological stage. The new indices provide a better understanding on the part of quantitative and quantitative information in Greenseeker index and lead to a more accurate leaf quantity estimation (from entire images), or specific physiological status characterization.
- Subjects :
- 0106 biological sciences
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing
NDVI
Multispectral image
Context (language use)
01 natural sciences
Vineyard
Normalized Difference Vegetation Index
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
In-field acquisition
[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture
Radiometric calibration
Mathematics
Remote sensing
Precision viticulture
Multi-spectral imaging
Foliage characterization
[ SDV.SA.STA ] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture
04 agricultural and veterinary sciences
Vegetation
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Precision agriculture
General Agricultural and Biological Sciences
010606 plant biology & botany
Subjects
Details
- Language :
- English
- ISSN :
- 13852256 and 15731618
- Database :
- OpenAIRE
- Journal :
- Precision Agriculture, Precision Agriculture, Springer Verlag, 2016, 18 (3), pp.293-308. 〈10.1007/s11119-016-9489-y〉, Precision Agriculture, Springer Verlag, 2016, 18 (3), pp.293-308. ⟨10.1007/s11119-016-9489-y⟩
- Accession number :
- edsair.doi.dedup.....36502ce65e131b27d7d3ac140d15b5a7
- Full Text :
- https://doi.org/10.1007/s11119-016-9489-y〉