Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a global destructive threat to forests and has led to serious economic losses all over the world. Therefore, it is necessary to establish a feasible and effective method to accurately monitor and estimate PWD infection. In this study, we used hyperspectral imagery (HI) collected by an unmanned airship with a hyperspectral imaging spectrometer to detect PWD in healthy, early, middle and serious infection stages. To avoid massive calculations on the full spectral dimensions of the HI, 16 spectral features were extracted from the HI, and a genetic algorithm (GA) was implemented to identify the optimal ones with the least fitness. Simultaneously, a support vector machine (SVM) classifier was established to predict the PWD infection stage for an individual pine tree. The following results were obtained: (1) the spectral characteristics for pine trees in different PWD infection stages were distinctive in the green region (510–580 nm), red edge (680–760 nm) and near-infrared (780–1000 nm) spectra; (2) the six optimal spectral features (Dgreen, SDgreen, Dred, DRE, DNIR, SDNIR) selected with the GA effectively distinguished the PWD infection stages of pine trees with a lower calculation cost; (3) compared with the traditional classifiers, such as k-nearest neighbor (KNN), random forest (RF) and single SVM, the proposed GA and SVM classifier achieved the highest overall accuracy (95.24%) and Kappa coefficient (0.9234). The approach could also be employed for monitoring and detecting other forest pests.
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Appl. Sci.
2022,
12(13), 6676; https://doi.org/10.3390/app12136676
green, SD
green, D
red, D
RE, D
NIR, SD
NIR) selected with the GA effectively distinguished the PWD infection stages of pine trees with a lower calculation cost; (3) compared with the traditional classifiers, such as k-nearest neighbor (KNN), random forest (RF) and single SVM, the proposed GA and SVM classifier achieved the highest overall accuracy (95.24%) and Kappa coefficient (0.9234). The approach could also be employed for monitoring and detecting other forest pests. View Full-Text
Keywords:pine wilt disease; hyperspectral imagery; GA; SVM; classification
Figure 1
MDPI and ACS Style
Zhang, S.; Huang, H.; Huang, Y.; Cheng, D.; Huang, J. A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery. Appl. Sci. 2022, 12, 6676. https://doi.org/10.3390/app12136676
AMA Style
Zhang S, Huang H, Huang Y, Cheng D, Huang J. A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery. Applied Sciences. 2022; 12(13):6676. https://doi.org/10.3390/app12136676
Chicago/Turabian Style
Zhang, Sulan, Hong Huang, Yunbiao Huang, Dongdong Cheng, and Jinlong Huang. 2022. "A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery" Applied Sciences 12, no. 13: 6676. https://doi.org/10.3390/app12136676
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