Abstract
Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings–BPNN model had the best performance, which modeling accuracy (RC2) and validation accuracy (RP2) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust.
Highlights
Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), and characterized by strong outbreaks, high incidence, wide occurrence area, and large economic losses, is one of the most prevalent wheat diseases, responsible for severe yield decreases worldwide [1,2,3]
If the effective diagnosis of wheat stripe rust leaves can be carried out accurately and quickly during the incubation period, prevention and control measures can be taken as early as possible, and the losses caused by the disease will be greatly reduced
There are some differences in the reflectance spectra of wheat leaves with a different number of days
Summary
Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), and characterized by strong outbreaks, high incidence, wide occurrence area, and large economic losses, is one of the most prevalent wheat diseases, responsible for severe yield decreases worldwide [1,2,3]. Wheat stripe rust, caused by Puccinia striiformis f. Tritici (Pst), and characterized by strong outbreaks, high incidence, wide occurrence area, and large economic losses, is one of the most prevalent wheat diseases, responsible for severe yield decreases worldwide [1,2,3]. Uredospores lurk in the interior of wheat leaves and absorb nutrients from the host to rapidly grow and reproduce; symptoms do not show, making it impossible for people to perceive the infection. If the effective diagnosis of wheat stripe rust leaves can be carried out accurately and quickly during the incubation period, prevention and control measures can be taken as early as possible, and the losses caused by the disease will be greatly reduced
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