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

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 952 ◽  
Author(s):  
Zhifeng Yao ◽  
Yu Lei ◽  
Dongjian He
Keyword(s):  
Chlorophyll Content ◽  
Hyperspectral Imaging ◽  
Stripe Rust ◽  
Near Infrared ◽  
Visual Detection ◽  
Early Stage ◽  
Hyperspectral Images ◽  
Successive Projections Algorithm ◽  
Wheat Stripe Rust ◽  
Wheat Leaves

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.

TaNAC2 is a negative regulator in the wheat-stripe rust fungus interaction at the early stage

2018 ◽  
Vol 102 ◽  
pp. 144-153 ◽  
Author(s):  
Xin-mei Zhang ◽  
Qiong Zhang ◽  
Chen-ling Pei ◽  
Xing Li ◽  
Xue-ling Huang ◽  
...  
Keyword(s):  
Stripe Rust ◽  
Early Stage ◽  
Rust Fungus ◽  
Negative Regulator ◽  
Wheat Stripe Rust ◽  
Stripe Rust Fungus

scholarly journals Application of Near-Infrared Spectroscopy to Quantitatively Determine Relative Content of Puccnia striiformis f. sp. tritici DNA in Wheat Leaves in Incubation Period

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yaqiong Zhao ◽  
Yilin Gu ◽  
Feng Qin ◽  
Xiaolong Li ◽  
Zhanhong Ma ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Incubation Period ◽  
Stripe Rust ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Puccinia Striiformis ◽  
Quantitative Detection ◽  
Support Vector ◽  
Infected Leaf ◽  
Wheat Leaves

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating wheat disease worldwide. Potential application of near-infrared spectroscopy (NIRS) in detection of pathogen amounts in latently Pst-infected wheat leaves was investigated for disease prediction and control. A total of 300 near-infrared spectra were acquired from the Pst-infected leaf samples in an incubation period, and relative contents of Pst DNA in the samples were obtained using duplex TaqMan real-time PCR arrays. Determination models of the relative contents of Pst DNA in the samples were built using quantitative partial least squares (QPLS), support vector regression (SVR), and a method integrated with QPLS and SVR. The results showed that the kQPLS-SVR model built with a ratio of training set to testing set equal to 3 : 1 based on the original spectra, when the number of the randomly selected wavelength points was 700, the number of principal components was 8, and the number of the built QPLS models was 5, was the best. The results indicated that quantitative detection of Pst DNA in leaves in the incubation period could be implemented using NIRS. A novel method for determination of latent infection levels of Pst and early detection of stripe rust was provided.

scholarly journals Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

2019 ◽  
Vol 9 (2) ◽  
pp. 331
Author(s):  
Peng Yu ◽  
Min Huang ◽  
Min Zhang ◽  
Bao Yang
Keyword(s):  
Hyperspectral Imaging ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Hyperspectral Images ◽  
Least Squares Regression ◽  
Wavelength Selection ◽  
Successive Projections Algorithm ◽  
Imaging Evaluation ◽  
Optimal Wavelength

Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400–1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of R p = 0.966 and R M S E P = 5.105 % , which is better than those of successive projections algorithm ( R p = 0.932 and R M S E P = 7.329 % ), and the uninformative viable elimination algorithm ( R p = 0.928 and R M S E P = 7.416 % ). The results obtained by BFWA were more stable, with a smaller standard deviation of R p and R M S E P than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying.

CHEMOMETRIC STRATEGIES FOR NEAR INFRARED HYPERSPECTRAL IMAGING ANALYSIS: CLASSIFICATION OF SEED GENOTYPES

2021 ◽  
Author(s):  
Simone Simões ◽  
Priscilla Rocha ◽  
Everaldo Paulo Medeiros ◽  
Carolina Silva
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Hyperspectral Images ◽  
Imaging Analysis

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based in HSI in the...

scholarly journals Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water

2020 ◽  
Vol 12 (13) ◽  
pp. 2070
Author(s):  
Geonwoo Kim ◽  
Insuck Baek ◽  
Matthew D. Stocker ◽  
Jaclyn E. Smith ◽  
Andrew L. Van Tassell ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Water Sampling ◽  
Floating Platform ◽  
Irrigation Pond

This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source.

scholarly journals Development of Race-Specific SCAR Markers for Detection of Chinese Races CYR32 and CYR33 of Puccinia striiformis f. sp. tritici

Plant Disease ◽  
2010 ◽  
Vol 94 (2) ◽  
pp. 221-228 ◽  
Author(s):  
Baotong Wang ◽  
Xiaoping Hu ◽  
Qiang Li ◽  
Baojun Hao ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Stripe Rust ◽  
Genomic Dna ◽  
Puccinia Striiformis ◽  
Random Amplified Polymorphic Dna ◽  
Scar Markers ◽  
Wheat Stripe Rust ◽  
Wheat Genotypes ◽  
Sequence Characterized Amplified Region ◽  
Wheat Leaves

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici, is a devastating disease in China. Races CYR32 and CYR33 have been predominant in the recent P. striiformis f. sp. tritici population. To develop molecular markers for these races, initially 86 isolates, most of which were collected in 2007 throughout China, were tested on the set of wheat genotypes for differentiating Chinese P. striiformis f. sp. tritici races, and their genomic DNA were amplified with 94 random amplified polymorphic DNA (RAPD) primers. Twelve isolates were identified as CYR33, 14 as CYR32, and 60 as 13 other races. A 320-bp band was identified to be associated with CYR32 with primer S1271 (5′-CTTCTCGGTC-3′), and a 550-bp band was identified to be specific to CYR33 with primer S1304 (5′-AGGAGCGACA-3′). The two bands were cloned and sequenced. Based on the sequences, sequence characterized amplified region (SCAR) markers CYR32sp1/sp2 and CYR33sp1/sp2 were developed to differentiate CYR32 and CYR33, respectively, from other races. The SCAR markers were validated with DNA samples from wheat leaves inoculated with selected isolates from the 86 isolates and urediniospore DNA samples from an additional 63 isolates collected from 2006 to 2009. The detection of CYR32 and CYR33 with the SCAR markers was completely consistent with the results of the race identification with the set of differential wheat genotypes. Thus, the markers are highly reliable for identification of the two races.

Monitor Disease Incidence of Winter Wheat Stripe Rust Using Hyperspectral Data: Analysis of Characteristic Spectrum and Establishing of Inversion Models

2014 ◽  
Vol 602-605 ◽  
pp. 2462-2467
Author(s):  
Mu Yi Huang ◽  
Wen Jiang Huang ◽  
Xiao Dong Yang ◽  
Guang Zhou Chen
Keyword(s):  
Winter Wheat ◽  
Stripe Rust ◽  
Vegetation Index ◽  
Near Infrared ◽  
Disease Incidence ◽  
Correlation Coefficients ◽  
Hyperspectral Data ◽  
Wheat Stripe Rust ◽  
Canopy Spectral Reflectance ◽  
Reflectance Data

It was discussed of the selection method of characteristic spectral band and the establishing of inversion model to monitor winter wheat stripe rust using hyperspectral data in this study. The correlation coefficients between the DI (disease incidence) at different stages of infection and the initial canopy reflectance spectral and the derivative of the reflectance spectrum were compared, respectively. The results showed that the derivative of the reflectance spectra has reached higher significant level with the DI than the initial reflectance spectral data. The initial reflectance in the visible light 680nm wavelength and the near infrared 976nm, 1010nm wavelength were selected to do regression with the DI of winter wheat stripe rust. And some inversion models between the DI and the hyperspectral data or its conversion patterns like NDVI (Normalized difference vegetation index), RVI (Ratio vegetation index), TVI (Transformed vegetation index) and its differential values of the canopy spectral reflectance data to monitor winter wheat stripe rust were established. Meanwhile, those correlation coefficients were compared respectively, of which we found the pattern of vegetation index has more efficient commonly than initial canopy spectral reflectance data by aggression analysis with the DI. The paper also suggested that the possibility of developing a special visible/near-infrared sensor for the detection of the DI of winter wheat stripe rust theoretically. Else, the SRSI (stripe rust stress index) mechanism model was presented for the first time in this paper.

Near-infrared hyperspectral image at a glance: Some personal thoughts

NIR news ◽  
2020 ◽  
Vol 31 (5-6) ◽  
pp. 8-14
Author(s):  
José Manuel Amigo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Constructive Criticism ◽  
Constructive Discussion

First of all, I want to transmit my most humble thanks to all people who believe that I deserve the “2019 Thomas Hirschfeld” award (kindly supported by FOSS) for my work on near-infrared spectroscopy and, especially, applied on hyperspectral images. I must confess that this award caught me by surprise and that I felt a bit overwhelmed when I received it. It is an honour full of respect and responsibility. I have been given the opportunity of writing this article, and I will profit it to express different personal thoughts about general but relevant aspects of near infrared applied to hyperspectral imaging. Also, since I am more a practitioner in chemometrics (or machine learning or data mining, or …) than a developer, I will also include some insights about the beautiful combination of near-infrared hyperspectral image with chemometrics. This article is just a glimpse of constructive criticism with personal thoughts that comes from my little experience in this field. Therefore, and of course, all opinions here are open for constructive discussion with the only purpose of learning (like the machines do nowadays).

scholarly journals Mid-Infrared Compressive Hyperspectral Imaging

2021 ◽  
Vol 13 (4) ◽  
pp. 741
Author(s):  
Shuowen Yang ◽  
Xiang Yan ◽  
Hanlin Qin ◽  
Qingjie Zeng ◽  
Yi Liang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Side Information ◽  
Computational Imaging ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Acquisition Time ◽  
Reconstruction Method ◽  
Mid Infrared ◽  
Spectral Channels

Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems.

Prediction of Chlorophyll Content in Wheat Leaves Using Hyperspectral Images

2010 ◽  
Author(s):  
Yankun Peng ◽  
Hui Huang ◽  
Wei Wang ◽  
Xiu Wang ◽  
Jianhu Wu ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Hyperspectral Images ◽  
Wheat Leaves
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