Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-22T14:17:08.614Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bauriegel, E, Giebel, A, Geyer, M, Schmidt, U and Herpich, W 2011b. Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computer Electronics Agriculture 75, 304–312.Google Scholar
Bauriegel, E, Giebel, A and Herpich, WB 2011a. Hyperspectral and chlorophyll fluorescence imaging to analyze the impact of Fusarium culmorum on the photosynthetic integrity of infected wheat ears. Sensors (Basel Switzerland) 11, 3765–3779.Google Scholar
Bauriegel, E and Herpich, WB 2014. Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. infections on wheat. Agriculture 4, 32–57.CrossRefGoogle Scholar
Bergsträsser, S, Fanourakis, D, Schmittgen, S, Cendrero-Mateo, MP, Jansen, M, Scharr, H and Rascher, U 2015. HyperART: Non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging. Plant Methods 11, 1–17.Google Scholar
Blackburn, GA 1998. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of Remote Sensing 19, 657–675.CrossRefGoogle Scholar
Broge, NH and Leblanc, E 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76, 156–172.Google Scholar
Chen, J 1996. Evaluation of vegetation indices and modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 22, 229–242.Google Scholar
Franke, J and Menz, G 2007. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture 8, 161–172.CrossRefGoogle Scholar
Garcia-Ruiz, F, Sankaran, S, Maja, JM, Lee, WS, Rasmussen, J and Ehsani, R 2013. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computer Electronics Agriculture 91, 106–115.Google Scholar
Genc, H, Genc, L, Turhan, H, Smith, S and Nation, 2008. Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat. African Journal of Biotechnology 7, 173–180.Google Scholar
Gitelson, AA and Merzlyak, MN 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18, 2691–2697.Google Scholar
Haboudane, D, Miller, JR, Pattey, E, Zarco-Tejada, PJ and Stra-Chan, I 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352.Google Scholar
Hock, J, Krans, J and Renfro, BL 1989. El ‘complejo “mancha de asfalto” del maíz, su distribución geográfica, requisitos ambientales e importancia económica en México. Revista Mexicana de Fitopatología 7, 129–135.Google Scholar
Hock, J, Kranz, J and Renfro, BL 1992. Tests of standard diagrams for field use in assessing the tarspot disease complex of maize (Zea mays). Tropical Pest Management 38 (3), 314–318.Google Scholar
Hock, J, Kranz, J and Renfro, BL 1995. Studies on the epidemiology of the tar spot disease complex of maize in Mexico. Plant Pathology 44, 490–502.CrossRefGoogle Scholar
Mahlein, AK, Steiner, U, Hillnhütter, C, Dehne, HW and Oerke, EC 2012. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet disease. Plant methods 2012, 8 (3).Google Scholar
Pereyda-Hernández, J, Hernández-Morales, J, Sandoval-Islas, S., Aranda-Ocampo, S, de León, C and Gómez-Montiel, N 2009. Etiología y manejo de la mancha de asfalto (Phyllachora maydis Maubl.) del maíz en Guerrero, México. Agrociencia 43 (5), 511–519.Google Scholar
Rondeaux, G, Steven, M and Baret, F 1996. Optimization of soil adjusted vegetation indices. Remote Sensing of Environment 55, 95–107.CrossRefGoogle Scholar
Rougean, JL and Breon, FM 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 51, 375–384.Google Scholar
Rouse, JW, Haas, RH, Schell, JA, Deering, DW and Harlan, JC 1974. Monitoring the vernal advancements and retrogradation of natural vegetation. NASA/GSFC, Greenbelt, MD.Google Scholar
Simko, I, Jimenez-Berni, JA and Sirault, XRR 2016. Phenomic approaches and tools for phytopathologists. Phytopathology 107 (1), 6–17.Google Scholar
Yang, C, Everitt, JH and Fernandez, CJ 2010. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystem Engineering 107, 131–139.Google Scholar
Zarco-Tejada, PJ, Berjón, A, López-Lozano, R, Miller, JR, Marin, P, Cachorro, V, Gonzales, MR and de Fruto, A 2005. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment 99, 271–287.CrossRefGoogle Scholar