early detection of fire blight disease of pome fruit trees using visiblenir spectrometry and dimensionality reduction methods

Fire blight (fb) is the most destructive bacterial disease of pome fruit trees around the world. in recent years, spectrometry has been shown to be an accurate and realtime sensing technology for plant disease detection. so, the main objective of this research is early detecting fb of pear trees by using visiblenearinfrared spectrometry. to get this goal, the reflectance spectra of healthy leaves (nd), nonsymptomatic (ns), and symptomatic diseased leaves (sy) were captured in the visible–nir spectral regions. in order to keep the important information of spectra and reduce the dimension of data, three linear and nonlinear manifoldbased learning techniques were applied such as, principal component analysis (pca), sammon mapping and multilayer autoencoder (mae). the output of manifoldbased learning techniques was used as an input of the simca (soft independent modeling by class analogy) classification model to discriminate ns and nd leaves. based on the results, the best classification accuracy obtained by using pca on the 1st derivative spectra, with accuracy of 95.8%, 89.3%, and 91.6% for nd, ns, and sy samples, respectively. these results support the capability of manifoldbased learning techniques for early detection of fb via spectrometry method.

Early Detection of Fire Blight Disease of Pome Fruit Trees Using VisibleNIR Spectrometry and Dimensionality Reduction Methods

Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and realtime sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by using VisibleNearinfrared spectrometry. To get this goal, the reflectance spectra of healthy leaves (ND), nonsymptomatic (NS), and symptomatic diseased leaves (SY) were captured in the visible–NIR spectral regions. In order to keep the important information of spectra and reduce the dimension of data, three linear and nonlinear manifoldbased learning techniques were applied such as, Principal Component Analysis (PCA), Sammon mapping and Multilayer autoencoder (MAE). The output of manifoldbased learning techniques was used as an input of the SIMCA (Soft independent modeling by class analogy) classification model to discriminate NS and ND leaves. Based on the results, the best classification accuracy obtained by using PCA on the 1st derivative spectra, with accuracy of 95.8%, 89.3%, and 91.6% for ND, NS, and SY samples, respectively. These results support the capability of manifoldbased learning techniques for early detection of FB via spectrometry method.