Hyperspectral imaging application under field conditions: assessment of the spatio-temporal variability of grape composition within a vineyard

In: Precision agriculture '19
Authors:
S. Gutierrez Televitis Research Group. University of La Rioja, 26007Logroño, Spain.

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M.P. Diago Televitis Research Group. University of La Rioja, 26007Logroño, Spain.

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J. Fernandez-Novales Televitis Research Group. University of La Rioja, 26007Logroño, Spain.

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J. Tardaguila Televitis Research Group. University of La Rioja, 26007Logroño, Spain.

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Type:
Chapter
Pages:
287–292
DOI:
https://doi.org/10.3920/978-90-8686-888-9_35

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This work illustrates how hyperspectral imaging, carried out from a moving ground vehicle, can be used under field conditions for assessing grape composition and evaluating its spatio-temporal variability within a vineyard. To analyse the distribution of the grape composition parameters, measurements were carried out at four different dates during the ripening season, at which total soluble solids (TSS) and anthocyanin concentrations analyses were also performed. Prediction models were generated for the two parameters, yielding 10-fold cross validation determination coefficients (R2) of 0.92 for TSS and 0.72 for anthocyanin concentrations (RMSEs of 1.444 °Brix and 0.272 mg/g berry, respectively). The results were used for mapping the evolution and distribution of grape composition in time-four dates-and space-the vineyard plot, illustrating the suitability of this technology for the in-field monitoring of the vineyard.

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Cover Precision agriculture '19
E-Book ISBN:
9789086868889
Publisher:
Wageningen Academic
Print Publication Date:
08 Jul 2019
Front Matter
Section 1 – Precision agriculture
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Section 2 – Precision horticulture
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Section 3 – Precision viticulture
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Section 4 – Precision crop protection
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Section 5 – Proximal and remote sensing of soil and crop
Design of a portable sensor suite for real-time monitoring of crop water stress index in maize breeding plots
Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging
Assessing infield temporal and spatial variability of leaf water potential using satellite imagery and meteorological data
Characterisation of cereal morphology through proximal stereo vision under contrasting nitrogen inputs
Early detection of corn and sunflower stress induced by chemical spraying
Potential of optical sensors for nitrogen management in spring barley
Disentangling the sources of chlorophyll-content variability in banana fields
Sentinel-2 vegetation indices and apparent electrical conductivity to predict barley (Hordeum vulgare L.) yield
Accessing the plant architecture in 3D for plant phenotyping - recent approaches and requirements
Vegetation indices from remote sensing imagery as proxies for yield and grain N in wheat
Using terrestrial photogrammetry for leaf area estimation in maize under different plant growth stages
Early detection of Fusarium infection in corn using spectral analysis
Proximal versus remote sensing to monitor pasture quality in a Mediterranean Montado ecosystem
Section 6 – Applications of unmanned aerial systems
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Section 7 – Satellite-based applications for precision agriculture
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Section 8 – Site-specific nutrient, lime and seed management
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Section 9 – Site-specific canopy and harvest quality
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Section 10 – Precision pasture management
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Section 11 – Drainage optimization and variable rate irrigation
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Section 12 – Geostatistics, mapping and spatial data analysis
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Section 13 – Robotics, guidance and automation
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Section 14 – Economics of precision agriculture
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Section 15 – Adoption of precision agriculture
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Section 16 – Small farms/small holders and precision agriculture
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Section 17 – On farm experimentation with site-specific technologies
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Section 18 – Software and mobile apps for precision agriculture
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Section 19 – Decision support for precision agriculture
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Section 20 – Data mining for precision agriculture
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Back Matter

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