Journal of Phytopathology最新文献

The poinsettia mosaic virus (PnMV) was first identified in Euphorbia milii in South Korea using nanopore sequencing; the complete genome of this isolate, named PnMV-CNU, has subsequently been assembled and characterised. The transcriptomic analysis results were further validated by RT-PCR using PnMV-specific primers and Sanger sequencing. The PnMV-CNU genome consisted of 6094 nucleotides, encoding a polyprotein with conserved domains typical of the Tymoviridae family, including methyltransferase, protease, helicase, RNA-dependent RNA polymerase and coat protein. Phylogenetic analysis demonstrated a close relationship between PnMV-CNU and other isolates, suggesting a broad geographical distribution of the virus. Mechanical inoculation of PnMV-CNU onto Chenopodium album confirmed its infectivity, resulting in systemic foliar necrosis on inoculated plants. To our knowledge, this study represents the first report of PnMV infecting E. milii, expanding the known host range of the virus and highlighting the importance of rapid diagnostic tools such as nanopore sequencing. These findings underscore the need for vigilant surveillance to mitigate the impact of PnMV on Euphorbia species.

Verticillium dahliae is a devastating pathogen that causes Verticillium wilt of Brassica napus. This study investigated the application of Pseudomonas putida and Trichoderma harzianum as biocontrol agents against V. dahliae, and their impact on the growth of B. napus was studied. In vitro, dual-culture assays revealed significant mycelial growth inhibition of V. dahliae by both T. harzianum (96%) and P. putida (83%). For in vivo studies, the fungus (T. harzianum) was directly introduced into V. dahliae-infested soil, and B. napus seeds were sown (T + VD). For the inoculation with bacteria, seeds of B. napus were primed with P. putida and sown in V. dahliae–infested soil (P + VD). Both treatments significantly improved physiological parameters (seed germination, root length, shoot length, relative water content and chlorophyll contents) and decreased relative electrolyte leakage and oxidative burst (malondialdehyde, H₂O₂). These treatments also increased the concentrations of osmolytes (proline and sugar content) and enhanced enzymatic activities (catalase, peroxidase and superoxide dismutase). After 21 days of germination, control and treated plants (inoculated only with T. harzianum or P. putida) displayed no wilting symptoms. V. dahliae–inoculated plants displayed severe symptoms of wilting, and the lower leaves turned yellow and showed curling. Treatment T + VD revealed almost no disease symptoms, while treatment P + VD exhibited negligible disease symptoms. Histological analysis revealed decreased mycelial colonisation of V. dahliae in the vascular system of B. napus in both T + VD and P + VD treatments. These findings successfully demonstrated the significance of both biocontrol agents for controlling the Verticillium wilt of B. napus.

Leaf rust (LR) poses a global threat to wheat crops and can lead to severe yield losses if environmental conditions favour its spread. Using resistant wheat cultivars offers a sustainable approach to managing LR. This study aimed to identify promising wheat lines for LR-resistance breeding using classical analytical methods to screen for LR tolerance. We evaluated 10 parental lines, comprising 6 lines and 4 testers, crossed into 24 combinations using a line × tester mating design. These germplasm were grown in a triplicate RCB design under both optimal and LR-stress conditions. We recorded data on various morphological, physiochemical, yield and component traits at key growth stages. The analysis of combining ability indicated significant variations among genotypes, with non-additive gene action influencing most traits. Four promising parents (AN179, AN1687, PR123 and PR127) and two crosses (AN179 × PR127 and AN179 × PR123) showed high combining ability for yield traits under LR-stress. Cluster analysis revealed divergent groups among the genotypes, with shifting clustering under LR-stress suggesting varied genotypic responses. Factor analysis identified genotypes that performed consistently well under LR-stress. These genotypes are suitable for use in LR-resistance breeding programs. We also recommend peduncle length and tillers per plant as phenotypic markers for wheat selection and breeding due to their positive correlation with grain yield. The findings of this study can contribute valuable insights to sustainable wheat breeding research.

In May 2016, leaf spot symptoms were observed on five-year-old common fig trees, located in Sari County (Mazandaran Province). Symptoms comprised irregular and brown necrotic spots, surrounded by yellow halos. Yellow-coloured, mucoid bacterial colonies were consistently isolated from the infected samples. Bacterial isolates were identified by using biochemical, molecular and pathogenicity assays. All isolates showed identical biochemical characteristics typical of the genus Pantoea. Furthermore, based on the nucleotide sequence of 16S rRNA and gyrB genes, the causal agent was identified as Pantoea agglomerans. Upon artificial inoculations under greenhouse conditions, the isolated strains caused symptoms in mature leaves of common fig saplings. To the best of our knowledge, this is the first worldwide report of P. agglomerans causing bacterial leaf spot on common fig.

Global agriculture is seriously threatened by plant diseases, which have an effect on output and food security. For disease care to be effective, prompt detection and precise diagnosis are essential. Traditional methods reliant on the visual inspection are labour-intensive and subjective. Recent technological advances in computer vision and machine learning offer promising solutions. This paper introduces the Transfer Learning-based Plant Disease Detection (TL-PDD) framework, which integrates preprocessing, segmentation, feature extraction and disease prediction stages. Initial preprocessing employs median filtering for data refinement. Segmentation, utilising the Adaptive Pixel Integration in Joint Segmentation (APIJS) approach, isolates disease-affected regions in plant images through a variant of DJS. Feature extraction includes the extraction of critical attributes such as Multi-texton, PHOG and Niblack's Method Assisted in Local Gabor Increasing Pattern (NMA-LGIP). Disease prediction employs a novel Double Convolutional Activation in Convolutional Neural Network-Transfer Learning (DCA-CNN-TL) model, facilitating disease classification and severity assessment based on extracted features. The efficiency and precision of plant disease detection systems can be improved by this framework, supporting efforts to ensure global food security and sustainable agriculture.

In the agricultural sector, pesticides are used to prevent disease transmission and protect crop yields. However, due to the diverse range of diseases, the human observation can often lead to misidentification. It is essential for a timely and precise disease classification approach without human intervention. Classifying the plant leaf diseases with an automated system is the significant need in this scenario. In this work, a hybrid classification model for the categorisation of plant leaf diseases is presented. Preprocessing, segmentation, feature extraction and classification of leaf diseases are the four steps in this method. In this work, crops such as grapes and mango are considered. Primarily, preprocessing the input image by utilising Gaussian filtering methods, which enhances the quality of image. The filtered image is then put through a segmentation process using the MBIRCH framework. The segmented image is then used to extract a number of features, including GLCM, ILGBHS, colour, shape and deep features using the VGG16 and AlexNet networks. Following the procedure, the hybrid model—which combines Bi-GRU and DCNN with TL—is applied to the acquired features, and the final classified result is determined by the enhanced fusion score method.

Lima bean (Phaseolus lunatus L.) is a crop of notable agricultural importance. However, its production is severely affected by anthracnose, a disease caused by the fungus Colletotrichum truncatum. This study aimed to investigate the genetic inheritance of anthracnose resistance in lima beans to support breeding efforts. Segregating populations (F₁ and F₂) derived from crosses between resistant and susceptible genotypes were used. All plants with their first pair of developed leaves were inoculated with a conidia suspension of the CT4 isolate of C. truncatum (10⁶ conidia/mL) to study their inheritance. Phenotypic data were collected and analysed to identify inheritance patterns and resistance loci. According to the chi-square (χ²) test, the segregating ratio of 1:0 (resistant:susceptible) was accepted for the F₁ generation, and the ratio of 3:1 (resistant:susceptible) was accepted in the F₂ generation. These results indicate that resistance to C. truncatum in lima beans is conditioned by a single gene, showing evidence of dominant monogenic inheritance. The results offer pathways to develop resistant cultivars, improving crop productivity and sustainability.

Colletotrichum scovillei causes anthracnose in chilli pepper worldwide, which is one of the most serious diseases affecting the production of pepper fruits. Although there are several studies on the Colletotrichum disease genes identified, there are still gaps in the understanding of the pathogenic genes and pathogenic mechanisms of Colletotrichum. In this study, two Colletotrichum strains, C. scovillei (Colletotrichum scovillei) C1 and C. scovillei CD showed different virulence against chill pepper, with C. scovillei C1 having a marked virulence defect compared to C. scovillei CD. To explore the genetic variation between the two strains, comparative genomic and transcriptome analyses were conducted to reveal the functional genes associated with the virulence. At the genome level, C. scovillei C1 was found to have a number of structural variation (SVs), insertion and deletion (InDels) and single nucleotide polymorphisms (SNPs) compared with C. scovillei CD. Analysis of DEGs (differentially expressed genes) between C. scovillei C1 and C. scovillei CD at the transcriptome level revealed 106 DEGs, including three upregulated effectors in C. scovillei CD, which might be the reasons for the high virulence of C. scovillei CD. In summary, our study revealed the genomic and transcriptomic mechanism involved in C. scovillei virulence in pepper, which contributes to the understanding of pepper anthracnose pathogenicity.

Brown spot disease, caused by Bipolaris oryzae, poses a significant threat to rice production, affecting both yield and quality. The present study aimed to investigate the major mycoflora associated with seeds of 11 paddy varieties and the effects of B. oryzae on seed health and quality, encompassing morphological characterisation, location and transmission studies and assessing seed infection under hydropriming and pregermination conditions. The results revealed that the major fungi associated with paddy seeds were B. oryzae, Aspergillus spp., Fusarium sp., Curvularia sp., Trichoconiella padwickii and Rhizopus sp. Among these, B. oryzae was the predominant fungus observed in all 11 rice varieties with the maximum seed infection. Studies on the cultural and morphological variations among the 11 isolates from B. oryzae revealed that they had diverse colony colour, growth patterns and conidial characteristics. Brown spot diseased seeds showed a substantial decline in germination (%) and seedling vigour with ADT (Aduthurai) 46 rice variety showing highest reduction in germination (47%), followed by ADT 42 (54%) compared to healthy seeds. Furthermore, brown spot infection was prevalent across all seed components tested—lemma, palea, endosperm and embryo at varying rates—, highlighting the comprehensive nature of the disease's impact on the seed structure. Disease progression studies using different methods revealed varying infection rates, with the test tube agar method demonstrating the highest assessment rates (44%–65%), followed by the blotter method (40%–46%) and sand method (18%–38%). Furthermore, lower brown spot pathogen infection from the seedling emerged after 30 days of sowing and was observed when diseased seeds were exposed to hydropriming conditions compared to pregerminated and untreated conditions. This study sheds light on the intricate dynamics of brown spot disease in rice which has negative impact on seed health, germination rate and, ultimately, rice productivity and quality.

Plants are the building blocks of nature and human beings. However, the excessive explosion of population and climate changes, some plants are extinct, and some are on the corner of extinction. Additionally, numerous species remain unexplored till now. Exploring the species in the traditional way are labor-intensive, time-consuming and require specialised expertise. So, it is a very challenging task. To overcome these challenges, various state-of-the-art approaches have been proposed. These approaches often face significant limitations related to accuracy, training and testing processes. This paper proposed a novel approach to species identification leveraging deep learning techniques, employing a weighted average methodology. The proposed approach utilises well known publicly available datasets like Malayakew (MK) and Leafsnap, to evaluate F1 score, recall, accuracy, and precision. In proposed approach we utilised pretrained Convolutional Neural Networks (CNNs) and Transfer Learning (TL) to enhance performance. Specifically, architectures such as NASNet, DenseNet121, ResNet50V2, Xception, VGG19 and VGG16 were employed in the experimental study. The proposed approach achieved an F1 score of 99.9%, recall of 100%, accuracy of 100% and precision of 100% on the MK dataset. On the Leafsnap dataset, the suggested approach achieved an F1 score of 94%, recall of 94%, accuracy of 93.5% and precision of 94%. These results demonstrate that the proposed approach significantly outperforms existing state-of-the-art works, offering a robust and efficient solution for species identification across diverse datasets.