Early detection of corn and sunflower stress induced by chemical spraying

In: Precision agriculture '19
Authors:
S. Gad Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.

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Y. Edan Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.

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T. Sandovsky Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.

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I. Harary Evogene, Rehovot, Israel.

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T. Nacson Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Rishon-LeZion, Israel.

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E. Kosover AgPlenus, Rehovot, Israel.

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A. Levi Bar Shalom AgPlenus, Rehovot, Israel.

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V. Alchanatis Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Rishon-LeZion, Israel.

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Type:
Chapter
Pages:
279–285
DOI:
https://doi.org/10.3920/978-90-8686-888-9_34

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Herbicides inhibit plant growth by affecting different bio-chemical pathways or bio-physical states. Stress that was induced by inhibiting lipid metabolism or photosynthesis on corn and sunflower was detected using leaf spectral reflectance. Leaf spectral signatures were measured at six time points during two weeks by a spectro-radiometer. Phenotypes were evaluated by an expert at the same time points. Spectral processing models were used to compare the spectral response of a control group to treated plants using statistical T-tests. Results revealed that spectral reflectance can be used to detect the stress induced by inhibiting photosynthesis on corn and sunflower at the same time that the first visual symptoms appeared. Also, early detection can be accomplished with a lower confidence two days before visual symptoms appear.

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