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Use of remote sensing technology in the assessment of resistance of maize to tar spot complex

Published online by Cambridge University Press:  01 June 2017

F. A. Rodrigues Jr. ,
P. Defourny ,
B. Gérard ,
F. San Vicente  and
A. Loladze
F. A. Rodrigues Jr.*
Affiliation:
International Maize and Wheat Improvement Center – CIMMYT, Mexico
P. Defourny
Affiliation:
Earth and Life Institute, Université Catholique de Louvain, Belgium
B. Gérard
Affiliation:
International Maize and Wheat Improvement Center – CIMMYT, Mexico
F. San Vicente
Affiliation:
International Maize and Wheat Improvement Center – CIMMYT, Mexico
A. Loladze
Affiliation:
International Maize and Wheat Improvement Center – CIMMYT, Mexico
*
† Email: F.A.Rodrigues@cgiar.org
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Abstract

Assessment of Tar Spot Complex (TSC) severity in maize breeding experiments is conducted visually and may sometimes result in inconsistencies due to human interpretation. Disease scoring using remote sensing technologies may help bring more precision to the phenotyping process. An experiment for assessment of grain yield losses due to TSC was conducted at the Aguafria Experimental Station of the International Center for Wheat and Maize Improvement – CIMMYT in Mexico. Twenty-five maize genotypes were planted in spring of 2016 under a fungicide treatment to control TSC development and no fungicide treatment in a square lattice design with three replications. Four flights were carried out using an Unmanned Aerial Vehicle (UAV) equipped with a multispectral (550, 660, 735, 790 nm) and a thermal camera, simultaneously with the visual disease scorings and the yield was measured after harvesting. The preliminary results of the study indicated that the use of remote sensing in disease resistance phenotyping may be as effective as visual disease scoring since both correlate highly with the grain yield. Structural and chlorophyll vegetation indices (VIs) proved to be a good alternative for the estimation of yield losses caused by TSC in experimental field conditions, which may be potentially used for screening for resistance to this disease in maize genotypes, hypothetically reducing the need for visual disease scoring in the field.

Keywords

multispectral imagesgenotype selectionimagery data extraction
Type
Crop Protection
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 259 - 263
Copyright
© International Maize and Wheat Improvement Centre (CIMMYT) 2017 

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