Potential of optical sensors for nitrogen management in spring barley

In: Precision agriculture '19
Author:
R. Hackett Teagasc, Crops Research Centre, Oak Park, Carlow, Ireland.

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Type:
Chapter
Pages:
293–298
DOI:
https://doi.org/10.3920/978-90-8686-888-9_36

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Fertiliser nitrogen (N) decisions in Ireland for spring barley are made based on historical information (e.g. previous crop yield and previous crop grown) and no account is taken of the growth or N dynamics of the current crop. Spectral reflectance measurements of crops can indicate the N status of crops and may be useful for guiding N inputs to barley crops in a more site-specific way. This work examined the potential of an active reflectance sensor to indicate biomass and N status characteristics of spring barley crops during the growing season. Results indicate that reflectance measurements were well related to both N uptake and nitrogen nutrition index in spring barley and may be useful to guide N inputs to the crop.

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