Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees
Huanglongbing (HLB) or citrus greening disease is one of the most important diseases affecting citrus orchards in Florida and other parts of the world. The first critical step for a successful control of HLB is its detection and diagnosis. Spectroscopy has proven to yield reliable results for its early detection, minimizing the time consumed for this process. This study presents a new approach of high-resolution aerial imaging for HLB detection using a low-cost, low-altitude remote sensing multi-rotor unmanned aerial vehicle (UAV). A multi-band imaging sensor was attached to a UAV that is capable of acquiring aerial images at desired resolution by adjusting the flying altitude. Moreover, the results achieved using UAV-based sensors were compared with a similar imaging system (aircraft-based sensors) with lower spatial resolution. Data comprised of six spectral bands (from 530 to 900nm) and seven vegetation indices derived from the selected bands. Stepwise regression analysis was used to extract relevant features from UAV-based and aircraft-based spectral images. At both spatial resolutions, 710nm reflectance and NIR-R index values were found to be significantly different between healthy and HLB-infected trees. During classification studies, accuracies in the range of 6785% and false negatives from 7% to 32% were acquired from UAV-based data; while corresponding values were 6174% and 2845% with aircraft-based data. Among the tested classification algorithms, support vector machine (SVM) with kernel resulted in better performance than other methods such as SVM (linear), linear discriminant analysis and quadratic discriminant analysis. Thus, high-resolution aerial sensing has good prospect for the detection of HLB-infected trees.
unmanned aerial vehicle classification support vector machine citrus disease