«On-the-go»multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices

«On-the-go»multispectral imaging system to characterize the development of vineyard foliage... 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 (NDVI GS ). 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

«On-the-go»multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-016-9489-y
Publisher site
See Article on Publisher Site

Abstract

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 (NDVI GS ). 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.

Journal

Precision AgricultureSpringer Journals

Published: Dec 10, 2016

References

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