Testing the spectral resolutions of the new multispectral sensors for detecting Phaeosphaeria leaf spot (PLS) infestations in maize crop
Maize is one of the most important subsistence and commercial crops in the world. In Africa, it is regarded as one of the most popular food crops. Recently however, significant losses due to Phaeosphaeria leaf spot (PLS) infestation have been reported. Therefore, techniques for early detection of PLS infestation are valuable for mitigating maize yield losses. Recently, remotely sensed datasets have become valuable in crop assessment. In this study, we sought to detect early PLS infestation by comparing the performance of commonly used higher spatial resolution sensors (WorldView, Quickbird, Sentinel series 2, RapidEye and SPOT 6) based on their spectrally resampled field spectra. Canopy training spectra were collected on leaves with signs of early infestation and healthy leaves spectral characteristics used for comparison. Training data was collected in 2013 growing season while test data was collected under similar conditions in 2014. The Random Forest algorithm was used to establish the Kappa and overall, user and producer's accuracies. Results showed that the RapidEye sensor with an overall classification accuracy of 86.96% and Kappa value of 0.76 performed better than the rest of the sensors while the Red, Yellow and Red-Edge bands were most useful for detecting early PLS infestation. The value of the RapidEye sensor in detecting early PLS infestation can be attributed to the optimally centred Red Red-Edge bands sensitive to changes in chlorophyll content, a consequent of PLS infestation on maize leaves. The study provides valuable insight on the value of existing sensors, based on their sensor characteristics in detecting early PLS infestation.
Keywords: Phaeosphaeria leaf spot, Remote Sensing, sensors Random Forest, Variable importance
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