Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques

Abstract

Airborne multispectral and hyperspectral imaging can be used to detect potentially diseased trees rapidly over a large area using unique spectral signatures. Ground inspection and management can be focused on these detected zones, rather than an entire grove, making it less labor-intensive and time-consuming. We propose a method to detect the areas of citrus groves infected with citrus greening disease [Huanglongbing (HLB)] using airborne hyperspectral and multispectral imaging. This would prevent further spread of infection with efficient management plans of infected areas. Two sets of hyperspectral images were acquired in 2007 and 2009, from different citrus groves in Florida. Multispectral images were acquired only in 2009. A comprehensive ground truthing based on ground measurements and visual check of the citrus trees was used for validating the results using 2007 images. In 2009, a more accurate polymerase chain reaction test for selected trees from ground truthing was carried out. With a handheld spectrometer, ground spectral measurements were obtained along with their degrees of infection. A hyperspectral imaging software (ENVI, ITT VIS) was used for the analysis. HLB infected areas were identified using image-derived spectral library, mixture tuned matched filtering (MTMF), spectral angle mapping (SAM), and linear spectral unmixing. The accuracy of the MTMF method was greater than the other methods. The accuracy of SAM using multispectral images (87%) was comparable to the results of the MTMF and also yielded higher accuracy when compared to SAM analysis on hyperspectral images. A possible inaccurate ground truthing for the grove in 2007 generated more false positives.