Spectral difference analysis and airborne imaging classification for citrus greening infected trees

Abstract

Citrus greening, also called Huanglongbing (HLB), became a devastating disease spread through citrus groves in Florida, since it was first found in 2005. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were acquired to detect citrus greening infected trees in 2007 and 2010. Ground truthing including field and indoor spectral measurement, infection status along with GPS coordinates was conducted for both healthy and infected trees. Ground spectral measurements showed that healthy canopy had higher reflectance in the visible range, and lower reflectance in the near-infrared (NIR) range than HLB infected canopy. Red edge position (REP) also showed notable difference between healthy and HLB canopy. But the difference in the NIR range and REP were comparably more sensitive to the environment or the background noise. Accuracy for separating HLB and healthy samples reached more than 90% when a simple REP threshold method was implemented in the ground reflectance datasets, regardless of field or indoor measurement; but it did not work well with the HS images because of its low spatial resolution. Support vector machine (SVM) was able to provide a fast, easy and adoptable way to build a mask for tree canopy. High positioning error of the ground truth in the 2007 HS image led to validation accuracy of less than 50% for most of classification methods. In the 2010 image from Southern Gardens (SG) grove, with better ground truth records, higher classification accuracies (about 90% in training sets, more than 60% in validation sets for most of the methods) were achieved. Disease density maps were also generated from the classification results of each method; most of them were able to identify the severely infected areas. Simpler classification methods such as minimum distance (MinDist) and Mahalanobis distance (MahaDist) showed more stable and balanced detection accuracy between the training and validation sets in the 2010 images. Their similar infection trend with ground scouted maps showed a promising future to manage HLB disease with airborne spectral imaging.