Quick and Accurate Detection and Quantification of Magnaporthe oryzae in Rice Using Real-Time Quantitative Polymerase Chain Reaction

A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information

BACKGROUND

Pepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images.

RESULTS

The results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%.

CONCLUSIONS

The research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

Early Detection and Temporal Dynamics of Pratylenchus scribneri Infection in Potato Roots Determined Using Quantitative PCR and Root Staining

The root-lesion nematode, Pratylenchus scribneri, is a migratory endo-parasitic nematode that impacts potato production on a large scale. Effective management of this nematode requires an understanding of its population dynamics alongside early detection. Typically, the nematode population estimates are made from infested soil; however, considering the endo-migratory lifestyle of this nematode, it also is crucial to determine the nematode population residing inside the host roots. In this study, a SYBR green-based quantitative real-time PCR (qPCR) assay was developed for detection and quantification of P. scribneri in potato roots. The assay used a previously reported primer pair (ITS-2F/ITS-2R), and it proved to be specific and sensitive, detecting as low as 1/128th equivalents of a P. scribneri individual per 0.2 g of potato roots. The robustness of the assay was reflected in high correlation observed between the P. scribneri densities determined microscopically and the densities detected by qPCR in artificially inoculated (R² = 0.93) and naturally infected (R² = 0.73) root samples. A time-course experiment conducted in the greenhouse using qPCR detected P. scribneri in potato roots as early as 5 days after planting. The results correlated well with the microscopic observations (R² = 0.80) and were complemented further with root staining. Additionally, three P. scribneri reproduction peaks were observed during one 3-month growth cycle of potato. Overall, the assay developed in this study is specific to P. scribneri in DNA extracts of root tissue and allows early detection and understandings of reproduction timings of this important nematode of potato.

Molecular Detection of Magnaporthe oryzae from Rice Seeds

Rice blast disease caused by the fungus Magnaporthe oryzae is one of the most devastating diseases of rice worldwide. Blast pathogen infects all stages of rice causing leaf, collar, neck, and panicle blast symptoms. Seedlings infested by M. oryzae serve as an inoculum source, which gradually causes the disease symptoms on rice leaves. Hence, for the blast disease management, it is crucial to detect the pathogen in rice seeds, particularly at the presymptomatic stage. Early pathogen diagnosis enhances the accuracy and timing of fungicide applications, thereby improving their efficiency. Moreover, detection of infested seeds is important for the quarantine purposes to ensure the flow of quality rice seeds to the market. In this chapter, a PCR-based assay is described to detect the blast pathogen from rice seeds. The ability of this molecular method in reliably detecting pathogens can prevent the spread of blast disease because of its increased sensitivity and the reduction of diagnostic time.

Recent Advances in Molecular Diagnostics of Fungal Plant Pathogens: A Mini Review

Phytopathogenic fungal species can cause enormous losses in quantity and quality of crop yields and this is a major economic issue in the global agricultural sector. Precise and rapid detection and identification of plant infecting fungi are essential to facilitate effective management of disease. DNA-based methods have become popular methods for accurate plant disease diagnostics. Recent developments in standard and variant polymerase chain reaction (PCR) assays including nested, multiplex, quantitative, bio and magnetic-capture hybridization PCR techniques, post and isothermal amplification methods, DNA and RNA based probe development, and next-generation sequencing provide novel tools in molecular diagnostics in fungal detection and differentiation fields. These molecular based detection techniques are effective in detecting symptomatic and asymptomatic diseases of both culturable and unculturable fungal pathogens in sole and co-infections. Even though the molecular diagnostic approaches have expanded substantially in the recent past, there is a long way to go in the development and application of molecular diagnostics in plant diseases. Molecular techniques used in plant disease diagnostics need to be more reliable, faster, and easier than conventional methods. Now the challenges are with scientists to develop practical techniques to be used for molecular diagnostics of plant diseases. Recent advancement in the improvement and application of molecular methods for diagnosing the widespread and emerging plant pathogenic fungi are discussed in this review.

Promising Perspectives for Detection, Identification, and Quantification of Plant Pathogenic Fungi and Oomycetes through Targeting Mitochondrial DNA

Fungi and oomycetes encompass many pathogens affecting crops worldwide. Their effective control requires screening pathogens across the local and international trade networks along with the monitoring of pathogen inocula in the field. Fundamentals to all of these concerns are their efficient detection, identification, and quantification. The use of molecular markers showed the best promise in the field of plant pathogen diagnostics. However, despite the unquestionable benefits of DNA-based methods, two significant limitations are associated with their use. The first limitation concerns the insufficient level of sensitivity due to the very low and uneven distribution of pathogens in plant material. The second limitation pertains to the inability of widely used diagnostic assays to detect cryptic species. Targeting mtDNA appears to provide a solution to these challenges. Its high copy number in microbial cells makes mtDNA an attractive target for developing highly sensitive assays. In addition, previous studies on different pathogen taxa indicated that mitogenome sequence variation could improve cryptic species delimitation accuracy. This review sheds light on the potential application of mtDNA for pathogen diagnostics. This paper covers a brief description of qPCR and DNA barcoding as two major strategies enabling the diagnostics of plant pathogenic fungi and oomycetes. Both strategies are discussed along with the potential use of mtDNA, including their strengths and weaknesses.

Responses of Leaf Cuticles to Rice Blast: Detection and Identification Using Depth-Profiling Fourier Transform Mid-Infrared Photoacoustic Spectroscopy

Cuticle is the first barrier for rice to resist blast fungus on the surface of the leaf. Studies on how the rice leaf cuticle responds to rice blast and attempts to perform early detection of rice blast are limited, and these two issues were explored in this study via depth-profiling Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS). Rice leaves with four different scales of injury (healthy leaves as CK, asymptomatic leaves from mildly diseased seedlings as S1, infected leaves with fewer than five lesions as S2, and infected leaves with more than 10 lesions as S3) were scanned by three moving mirror velocities 0.32, 0.47, and 0.63 cm/s for the depth profiling of the rice leaf surface. The response patterns were acquired via chemometrics to analyze the variations of the chemical group absorptions in the different layers of a sample and in the same layer between different samples. Results showed that the leaf cuticle tended to be thicker and the relative content of fatty alcohols and cutin, unsaturated compounds, and aromatics in the cuticle increased when rice seedlings were infected by blast fungus. Together with the principal component analysis, the probabilistic neural network was applied to identify the samples in early stages (CK and S1), which reached an accuracy of 90% for the samples in the greenhouse and 82% for the samples in the field. Thus, depth-profiling FTIR-PAS was good at analyzing the variation in cuticle layers and showed great potential in the early detection of rice blast or other diseases in different species.

Latent Infection of Valsa mali in the Seeds, Seedlings and Twigs of Crabapple and Apple Trees is a Potential Inoculum Source of Valsa Canker

A real-time quantitative PCR assay using a species-specific primer pair was developed to rapidly and accurately quantify Valsa mali, the causative pathogen of apple Valsa canker (AVC), in crabapple seeds, crabapple seedlings, apple twigs and apple seeds. Surveys were conducted in different regions, and crabapple or apple seeds were collected for V. mali detection by qPCR assay. Our results showed that 12.87% to 49.01% of crabapple seeds collected from different regions were positive for V. mali. The exopleura and endopleura were the two major areas of V. mali infection in crabapple seeds. The presence of V. mali infection in crabapple seeds was also confirmed by a high-throughput sequencing approach. With the growth of crabapple seedlings, the concentration of V. mali gDNA in crabapple seedlings gradually increased until eight or more leaf blades emerged. One-year-old twigs from an apple scion nursery were infected with V. mali, and only apple seeds from infected apple trees showing evident Valsa canker symptoms carried V. mali. In conclusion, this study reports that crabapple seeds and apple seeds carried V. mali as latent inoculum sources. V. mali infected not only apple tissues but also crabapple seedlings, which are the rootstocks of apple trees. This study indicated that the inoculum sources for AVC vary. Application of a novel qPCR assay can potentially improve the accuracy of early diagnosis, and is helpful to reveal the epidemic regularity of AVC.

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging

Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host-pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management.

Impacts of elevated CO2 on exogenous Bacillus thuringiensis toxins and transgene expression in transgenic rice under different levels of nitrogen

Recent studies have highlighted great challenges of transgene silencing for transgenic plants facing climate change. In order to understand the impacts of elevated CO2 on exogenous Bacillus thuringiensis (Bt) toxins and transgene expression in transgenic rice under different levels of N-fertilizer supply, we investigated the biomass, exogenous Bt toxins, Bt-transgene expression and methylation status in Bt rice exposed to two levels of CO2 concentrations and nitrogen (N) supply (1/8, 1/4, 1/2, 1 and 2 N). It is elucidated that the increased levels of global atmospheric CO2 concentration will trigger up-regulation of Bt toxin expression in transgenic rice, especially with appropriate increase of N fertilizer supply, while, to some extent, the exogenous Bt-transgene expression is reduced at sub-N levels (1/4 and 1/2N), even though the total protein of plant tissues is reduced and the plant growth is restricted. The unpredictable and stochastic occurrence of transgene silencing and epigenetic alternations remains unresolved for most transgenic plants. It is expected that N fertilization supply may promote the expression of transgenic Bt toxin in transgenic Bt rice, particularly under elevated CO2.

Development of SCAR Markers and an SYBR Green Assay to Detect Puccinia striiformis f. sp. tritici in Infected Wheat Leaves

Stripe rust, caused by the pathogenic fungus Puccinia striiformis f. sp. tritici, is an important disease of wheat worldwide. A rapid and reliable detection of the pathogen in latent infected wheat leaves is useful for accurate and early forecast of outbreaks and timely application of fungicides for managing the disease. Using the previously reported primer pair Bt2a/Bt2b, a 362-bp amplicon was obtained from P. striiformis f. sp. tritici and a 486-bp amplicon was obtained from both P. triticina (the leaf rust pathogen) and P. graminis f. sp. tritici (the stem rust pathogen). Based on the sequence of the 362-bp fragment, two pairs of sequence characterized amplified region (SCAR) primers were designed. PSTF117/PSTR363 produced a 274-bp amplicon and TF114/TR323 produced a 180-bp amplicon from P. striiformis f. sp. tritici, whereas they did not produce any amplicon from P. triticina, P. graminis f. sp. tritici, or any other wheat-infecting fungi. The detection limit of PSTF117/PSTR363 was 1 pg/µl and TF114/TR323 was 100 fg/µl. Both SCAR markers could be detected in wheat leaves 9 h post inoculation. An SYBR Green RT-PCR method was also developed to detect P. striiformis f. sp. tritici in infected leaves with the detection limit of 1.0 fg DNA from asymptomatic leaf samples of 6 h after inoculation. These methods should be useful for rapid diagnosis and accurate detection of P. striiformis f. sp. tritici in infected wheat leaves for timely control of the disease.