Detection and mapping of trees infected with citrus gummosis using UAV hyperspectral data

Abstract

Monitoring citrus diseases and pests in early stages is fundamental to ensure the efficiency of phytosanitary control and plant health. The various diseases caused by fungi, bacteria, viruses, and pests limit citrus production. Citrus gummosis disease, caused by the fungus Phytophthora spp., is the main fungal disease of citrus in Brazil. The lesions caused to the trunk and roots by Phytophthora spp. lead losses in production, foot and root rot, brown fruit rot, canopy discoloration and leaf yellowing. Remote sensing is a nondestructive detection technology, that has been used to detect phytosanitary problems in agricultural crops. Multi and hyperspectral sensors on board unmanned aerial vehicles (UAVs) have been extensively applied in agriculture. In this study, the capability for the detection of citrus gummosis was evaluated in two data sets. The first one considered hyperspectral images acquired with a 25 band sensor covering a spectral range from 500 nm to 840 nm, and the second data set was a simulated 3 band of multispectral sensor. The results indicated a better performance for the detection of citrus gummosis with the hyperspectral images than with three bands multispectral images. The high dimensionality of the hyperspectral data and the detailed spectral information allowed a more accurate classification of citrus gummosis infected plants. The classification maps were validated with field data and achieved an accuracy of 0.79 (F-score = 0.55) for the health map produced with multispectral data and an accuracy of 0.94 (F-score = 0.85) for the health map produced by the hyperspectral data.