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Sensors 2019, 19(4), 952; https://doi.org/10.3390/s19040952

Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging

1,2,3, 1,2,3 and 1,2,3,*
1
College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
2
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China
3
Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Xianyang 712100, China
*
Author to whom correspondence should be addressed.
Received: 22 January 2019 / Revised: 12 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
(This article belongs to the Section Remote Sensors)
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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. View Full-Text
Keywords: wheat stripe rust; hyperspectral imaging; incubation period; SPAD; spatial distribution wheat stripe rust; hyperspectral imaging; incubation period; SPAD; spatial distribution
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Yao, Z.; Lei, Y.; He, D. Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging. Sensors 2019, 19, 952.

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