Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1015–1022, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1015-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1015–1022, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1015-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

USABILITY, STRENGTH AND PRACTICALITY OF THE UPCOMING HYSPIRI IN DETECTING MAIZE GRAY LEAFY SPOT IN RELATION TO SENTINEL-2 MSI, VENμS AND LANDSAT 8 OLI SPECTRAL BAND-SETTINGS

M. Sibanda1, O. Mutanga1, T. Dube2, J. Odindi1, and P. L. Mafongoya1 M. Sibanda et al.
  • 1University of KwaZulu-Natal, School of Agricultural, Earth and Environmental Sciences, Geography Department, Pietermaritzburg, South Africa
  • 2Institute for Water Studies, Dept. of Earth Sciences, The University of the Western Cape, Private Bag X17, Bellville 7535, South Africa

Keywords: hyperspectral data, maize, crop diseases, PLS-DA

Abstract. Considering the high maize yield loses that are caused by diseases incidences as well as incomprehensive monitoring initiatives in the crop farming sector of agriculture, there is a need to come up with spatially explicit, cheap, fast and consistent approaches for monitoring as well as forecasting food crop diseases, such as maize gray leaf spot. This study, therefore, we sought to investigate the usability, strength and practicality of the forthcoming HyspIRI in detecting disease progression of Maize Gray leafy spot infections in relation to the Sentinel-2 MSI, Landsat 8 OLI spectral configurations. Maize Gray leafy spot disease progression that were discriminated based on partial least squares –discriminant analysis (PLS-DA) algorithm were (i) healthy, (ii) intermediate and (ii) severely infected maize crops. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93 and 0.89 exhibited by Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor sensors, respectively. Further, the results showed that the visible section the red-edge and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray leafy spot infections. These findings underscore the added value and potential scientific breakthroughs likely to be brought about by the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop disease epidemics to ensure food security.