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

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.

Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques

“…In 2015, light-emitting diodes (LEDs) with an emission peak at 470 nm were used by Mahlein and colleagues to establish an illumination array. This array could improve the sensitivity of a hyperspectral imager in the blue spectrum [ 98 ].…”

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

“…Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1][2][3][4], the stress response [5][6][7], plant count [8,9], yield prediction [10][11][12], chlorophyll content [13][14][15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19][20][21][22][23].…”

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

“…Specifically, multispectral images have been used to successfully monitor the growth cycle of wheat, encompassing information about the crop photosynthetic light-use efficiency, leaf chlorophyll content and water stress [23][24][25]. With a much higher band number and narrower bandwidth, hyperspectral data could provide more detailed spectral information and could discriminate objects that may be unwittingly grouped by multispectral sensors [26][27][28].…”