Journal of Phytopathology最新文献

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.

Tomato leaf curl New Delhi virus (ToLCNDV), a begomovirus, that causes severe leaf curling, stunting, and reduced yield in tomato plants is consistently threatening its production worldwide. CRISPR/Cas9-mediated genome editing has shown immense potential in developing disease-resistant crops. This study successfully focuses on designing a precise and efficient strategy for in planta defence against ToLCNDV. Five key targets within the viral genome, essential for its replication and pathogenicity, were selected. Five Cas9-expressing constructs, along with the ToLCNDV infectious clone, were agroinfiltrated into tomato plants. Three constructs effectively disrupted the ToLCNDV genome. These three constructs, 1T, 2T, and 4T, were shortlisted based on symptom severity level. They showed a relatively low viral titre of 0.5, 0.42, and 0.25 through quantitative real time PCR (qPCR) after 3, 6, and 9 days of post-co-infiltration, respectively. Positive control plants showed significant signs of infection like yellowing of leaves, thickening of veins, and majorly upward curling of leaves. In comparison, plants infiltrated with three Cas9 constructs had mild yellowing of leaves that recovered after approximately 21-dpi. Furthermore, we assessed the agronomic performance of Cas9-mediated tomato plants through in planta Agrobacterium-mediated transformation with three short-listed guided RNA (gRNA) constructs under greenhouse conditions. Also, qPCR analysis of Cas9 protein in 7, 14, and 21-day of intervals gave a relative expression of 0.85, 0.76 and 0.51 respectively in genetically engineered (GE) plants through in planta transformation. In conclusion, this research contributes to CRISPR-Cas9-mediated plant genome editing. Our findings substantiate the efficacy of the CRISPR/Cas9 system in achieving durable engineering of resistance against ToLCNDV with 30% transformation efficiency in tomato plant. Furthermore, this study illuminates potential avenues for extending the application of this technology to confer resistance against singular and multiple infectious viruses in diverse crop species.

Biological control has emerged as a leading approach in managing crop diseases including Fusarium wilt of cashew caused by Fusarium oxysporum f.sp. anacardi. However, this approach is limited by the high cost of commercially available growth media and ultimately lacks applications at the farmer's level. The study examined three organic-based substrate formulations (rice bran, rice husk and their combination) of four combined Bacillus strains (Bacillus subtilis 4/5021 and Bacillus velenzesis 10/5140, 11/A + 1 and 13/A + 3) or Trichoderma asperellum to control the disease. The experiments were conducted in farmers' fields in 2021/2022 and 2022/2023. The treatments were applied once during the rainy season by soil drenching at a rate of 20 mL of bioformulation per litre of water around each tree. The results revealed that the treated cashew trees had significantly (p ≤ 0.05) lower final disease severity (between 15.17% and 33.75% in 2021/2022 and 14.43%–31.93% in 2022/2023) than the untreated trees (77.17% and 89.31%, respectively). The only treatment that was not significantly different from the control was the rice husk–T. asperellum formulation in both years. In treated plots, disease severity decreased over time each year, whereas disease severity increased in untreated trees. Based on these results, the three formulations of Bacillus strains and two of the Trichoderma asperellum formulations (bran-based or bran–husk combination) should be evaluated further for the management of Fusarium wilt of cashew. This study offers a potential solution to utilise locally available organic substrates for developing liquid-based formulations of biological control agents.

Tef head smudge (Curvularia miyakei) is an economically important plant disease in the warm-humid regions of the Western Amhara Region, Ethiopia. Therefore, this study aimed to assess the geographical distribution, habitat suitability and epidemiological factors influencing the incidence and severity of Tef head smudge disease. The result of the study depicted that Tef head smudge disease is widely distributed across the region at varying levels of incidence and severity. Sowing date, soil type, altitude and growth stage are the key epidemiological factors contributing to the variability in the incidence and severity of Tef head smudge disease. Moreover, isothermally, temperature seasonality and minimum temperature of the coldest month bioclimatic variables significantly influenced Tef head smudge disease dynamics. Our study also predicted the current habitat suitability of Tef head smudge disease using the MaxEnt (maximum entropy) species distribution model. The model was good in predicting Tef head smudge disease with an AUC (area under the Receiver Operating Curve) of 0.85. According to the model, 31.18%, 44.46% and 14.33% of the areas have highly suitable, moderately suitable and low suitable suitability respectively, whereas 10.03% of the areas have unsuitable suitability to Tef head smudge disease. This result underscores that a significant portion of the Western Amhara Region (75.64%) and similar agro-ecologies are at risk of Tef head smudge disease outbreaks. Therefore, it is important to implement targeted breeding programs and disease management strategies to ensure food security in regions where tef is a primary food source and Tef head smudge is prevalent.