Haiguang Wang

Identification and Disease Index Inversion of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data at Canopy Level

Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built using K-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.

Spatiotemporal effects of cultivar mixtures on wheat stripe rust epidemics

The use of cultivar mixtures is increasingly practical in wheat stripe rust management. Field experiments with wheat cultivar mixtures were conducted to determine their effects on temporal and spatial patterns of stripe rust epidemics in three regions. In the Beijing and Gangu fields, where the epidemics were caused by artificial inoculation, disease incidence and the area under the disease progress curve (AUDPC) of the cultivar mixtures were significantly lower (P < 0.05) than those of the susceptible pure stands. We defined the relative effectiveness of cultivar mixture on disease development related to that in pure stands (REM). The results demonstrated that in many treatments of mixtures of susceptible cultivar with resistant cultivars at various ratios in different locations, their effects on disease reduction were positive (REM < 1). The reduction of epidemic rate in cultivar mixtures expressed in either early season or late season depended on the initial pattern of disease and cultivar mixture treatments. Semivariograms were used to determine the spatiotemporal patterns of disease in the Gangu field. The spatial analysis showed clear spatial patterns of the disease in all four directions of the fields on susceptible pure stands but not on cultivar mixtures. The results implied that the mechanisms of cultivar mixture on disease management might include the interruption of disease spatial expansion and a physical barrier to pathogen inoculum by resistant cultivars.

Effects of UV-B Radiation on Near-Infrared Spectroscopy and Identification of Puccinia striiformis f. sp. tritici

Based on near-infrared spectra of three physiological races of Puccinia striiformis f. sp. tritici (i.e., CYR31, CYR32, and CYR33) irradiated under four UV-B intensities (i.e., 0, 150, 200, and 250 μw/cm2), the effects of UV-B radiation on near-infrared spectroscopy of the pathogen were investigated in spectral region 4000–10000 cm−1, and support vector machine models were built to identify UV-B radiation intensities and physiological races, respectively. The results showed that the spectral curves under UV-B radiation treatments exhibited great differences compared with the corresponding control treatment (0 μw/cm2) in the spectral regions 5300–5600 cm−1 and 7000–7400 cm−1 and that the absorbance values of all the three physiological races increased with the enhancement of UV-B radiation intensity. Based on near-infrared spectroscopy, different UV-B radiation intensities could be identified and different physiological races could be distinguished from each other with high accuracies. The results demonstrated the utility and stability of the proposed method to identify the physiological races.

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

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.

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

Quantitative Determination of Germinability of Puccinia striiformis f. sp. tritici Urediospores Using Near Infrared Spectroscopy Technology

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is an important disease on wheat. In this study, quantitative determination of germinability of Pst urediospores was investigated by using near infrared reflectance spectroscopy (NIRS) combined with quantitative partial least squares (QPLS) and support vector regression (SVR). The near infrared spectra of the urediospore samples were acquired using FT-NIR MPA spectrometer and the germination rate of each sample was measured using traditional spore germination method. The best QPLS model was obtained with vector correction as the preprocessing method of the original spectra and 4000–12000 cm−1 as the modeling spectral region while the modeling ratio of the training set to the testing set was 4 : 1. The best SVR model was built when vector normalization was used as the preprocessing method, the modeling ratio was 5 : 1 and the modeling spectral region was 8000–11000 cm−1. The results showed that the effect of the best model built using QPLS or SVR was satisfactory. This indicated that quantitative determination of germinability of Pst urediospores using near infrared spectroscopy technology is feasible. A new method based on NIRS was provided for rapid, automatic, and nondestructive determination of germinability of Pst urediospores.

Killing the teliospores of tilletia controversa Kuhn (TCK) with atmospheric microwave plasma

Summary form only given as follows. Sterilization of foodstuffs by microwave plasma may be an efficient, environmental-friendly method, but the plasma needed cannot be attained easily. With the proprietary cavity of atmospheric microwave plasma called APMPS+JET, we have been able to gain the atmospheric microwave plasma of the gas mixture (air and argon), by which the teliospores of Tilletia controversa Kuhn (TCK) were treated. These samples were seeded on pieces of cover glasses and then exposed in the APMPS+JET for less than 0.5 second. After the short time treatment, the teliospores were then observed by a scanning electron microscope (SEM), which showed that the teliospores were broken into pieces. The various factors, such as the UV, the heat, the microwaves, the high-voltage, the high-speed particles and the activated particles, were then discussed and the experimental results showed that the high-speed moving particles and the activated particles might play an active role in the teliospore-plasma interaction. These treatment results support the potential for use of the microwave cavity as a treatment of foodstuffs for pathogen reduction. Besides, this application can be useful to the food packaging industry, the medical industry, and others.

Atmospheric continuous filament discharge plasma (ACFDP) applied to crop sterilizatinon

Summary form only given. The ability of killing some microorganisms by atmospheric pressure glow discharges (APGD) has been demonstrated in a number of previous researches and can be extensively applied to medical treatment, sanitation, environmental protection and food reservation. At present, we designed a set of plasma device, which produce the dielectric barrier discharge plasma between the coaxial bar electrode and barrel electrode. This device adopts alternating current supply of 10 kHz to produce atmospheric continuous filament discharges plasma (ACFDP) at the gap distance of 5 mm and voltage of 15 kV. Because of the high exciting frequency, the plasma is generated constantly. We install nine of these devices in parallel and the applied power required to sustain stable discharges is around 400 W. When working gas (nitrogen, air, helium, etc) is inflated at a relatively high speed, the particle density decreases and the evenness degree of plasma increases, which is similar to the glow discharge. We use this plasma to treat some bacteria and fungus in crops, especially in wheat and the sterilization result is demonstrated to be remarkable