Abstract
Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. 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, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.
Highlights
In Sub-Saharan Africa, maize is the most important cereal crop farmed on over 25 million ha, mainly by smallholder farmers
The ANOVA results showed that there were significant differences (p = 0.005) between the healthy, intermediately infected, and severely infected maize crops, based on data resampled to the spectral settings of Hyperspectral InfraRed Imager (HyspIRI), Sentinel-2MSI, VENμS, and Landsat Operational Land imager (OLI)
Gray Leaf Spot (GLS) levels in maize exhibited less exhibited than 5% allocations omission, results of this study showed that HyspIRI was stronger and more accurate in of disagreement for infected only and optimal allocations of ofdiscriminating disagreement for the(commission) only(Figure andthe optimal allocations of agreement forless all the different GLS levels ininfected maize crops
Summary
In Sub-Saharan Africa, maize is the most important cereal crop farmed on over 25 million ha, mainly by smallholder farmers. These smallholder maize farms yield beyond 38 million metric tons of grain, mainly for subsistence purposes [1]. The production of maize has largely been stalled by pests and diseases that affect maize crops during the growing, post-harvesting processing, and storage stages. The disease is caused by a Cercospora zeae-maydis fungus, which affects maize crops across all the Agronomy 2019, 9, 846; doi:10.3390/agronomy9120846 www.mdpi.com/journal/agronomy. According to Dhami, et al [2], Cercospora zeae-maydis is a polycyclic facultative pathogen or fungi which survives as mycelium in the residues of infected maize crops after harvesting
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