Using remote sensing to map in-field variability of peanut maturity

In: Precision agriculture '19
Authors:
A.F. Santos São Paulo State University (UNESP), Jaboticabal Campus, Jaboticabal, São Paulo, Brazil.

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L.N. Lacerda University of Georgia, Tifton Campus, Tifton, Georgia, USA.

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S. Gobbo University of Georgia, Tifton Campus, Tifton, Georgia, USA.

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A. Tofannin University of Georgia, Tifton Campus, Tifton, Georgia, USA.

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R.P. Silva São Paulo State University (UNESP), Jaboticabal Campus, Jaboticabal, São Paulo, Brazil.

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G. Vellidis University of Georgia, Tifton Campus, Tifton, Georgia, USA.

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Type:
Chapter
Pages:
605–611
DOI:
https://doi.org/10.3920/978-90-8686-888-9_75

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A study was conducted to assess if vegetation indices (VIs) could be used as indicators of peanut maturity. A 24 ha block of a rainfed field with clearly visible soil and crop variability was used. An unmanned aerial vehicle (UAV) equipped with a multispectral camera captured spectral reflectance from the peanut canopy. The reflectance data were used to evaluate several VIs as potential indicators of peanut maturity. Pearson’s correlation and linear regression were used to evaluate the response of the VIs as well as the sensitivity of individual reflectance bands to peanut maturity. The red edge band was the most sensitive. The most responsive VIs were the non-linear index (NLI) and the modified non-linear index (MNLI) when red edge reflectance was substituted for red reflectance.

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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
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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
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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