Early detection of Fusarium infection in corn using spectral analysis

In: Precision agriculture '19
Authors:
T. Sandovsky Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

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Y. Edan Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

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S. Gad Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

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A. Etzioni Evogene, 13, Gad Feinstein, Rehovot 76120, Israel.

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T. Nacson Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Rishon-LeZion, 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:
339–346
DOI:
https://doi.org/10.3920/978-90-8686-888-9_42

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This work presents a non-destructive methodology for early detection of Fusarium infection, by spectral analysis in the 350-2,500 nm range. Corn plants in greenhouse conditions were analysed using spectral analysis. The Lasso model was used to differentiate infected from non-infected plants based on the first derivative of leaf spectral reflectance. Fusarium infection was successfully recognized in plants at V2 growth stage with 74% success rate. This result enables infection detection at a stage which currently is not possible without destroying the plant, which can be further applied to map the disease in field scale.

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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
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
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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