Journal of Phytopathology最新文献

Kiwifruit postharvest rot causes significant economic loss during handling process from field to table. In our attempt to identify pathogens associated with kiwifruits in 2019, about a quarter of cold-stored kiwifruits collected from Guizhou, China, were observed suffering postharvest rot. Diseased tissues were sampled for pathogen isolation. About 20% of the isolated pure cultures were identified to be Trichothecium roseum based on morphology and ITS sequence similarity. Identity was further supported by phylogeny analysis based on the LSU and TEF1α loci. Pathogenicity was confirmed on wounded kiwifruits and similar symptoms were observed. Koch's postulate was fulfilled by re-isolation of T. roseum from symptomatic inoculated fruits. To our knowledge, this is the first solid confirmation of this pathogen infecting kiwifruits and resulting in postharvest rot.

Neck blast is one of the most damaging fungal disease of rice, causing severe yield losses in the North-Western region of Haryana, India. Due to the lack of resistant cultivars commonly grown in Haryana, the management of the disease has relied predominantly on the foliar application of fungicides. Therefore, this study evaluated the field efficacy of various fungicides against rice neck blast in the North-Western region of Haryana, India, over the kharif seasons of 2020, 2021 and 2022. Fungicides were applied twice per season, at panicle emergence and 10 days later. Among the seven fungicides tested, isoprothiolane 40% EC, propineb 70% WP and difenoconazole 25% EC significantly reduced neck blast severity by 63.2%, 55.2% and 47.3% in 2020; 60.6%, 51.4% and 47.4% in 2021; and 70.0%, 64.9% and 62.5% in 2022, respectively with corresponding reductions in area under disease progress curve values compared with the untreated control. The highest grain yield was recorded for isoprothiolane 40% EC, followed by propineb 70% WP and difenoconazole 25% EC. The greatest increase in grain yield was observed in kharif 2020, whereas the lowest yield in kharif 2022 was associated with moderate total rainfall (284 mm) and high average relative humidity (94.4%) following the appearance of neck blast. These results suggest that these fungicides are effective in reducing rice neck blast and enhancing grain yield in the North-Western region of Haryana, India.

This study aimed to develop and validate a standard area diagram set (SADs) to estimate the severity of angular leaf spot (ALS), caused by the fungus Pseudocercospora griseola, in the leaflets of common bean plants. For the elaboration of SADs, the minimum and maximum disease severity limits in the leaflets were considered, along with the intermediate levels following linear increments. The SADs had 10 levels of severity as follows: 0.5%, 2.2%, 4.5%, 9.6%, 20.1%, 30.8%, 41.2%, 52.1%, 67.4%, and 77.3%. The validation was performed by 20 raters, 10 inexperienced and 10 experienced in quantifying severities of foliar diseases. The severity estimates were obtained in 150 leaflets displaying different levels of severity without and using the proposed SADs. Accuracy, precision and reliability of the estimates were improved using SADs. The SADs obtained in the present study proved to be adequate to estimate the severity of ALS in the leaflets of common bean plants with the potential of use in any study in which it is crucial to quantify the severity of this foliar disease to discriminate treatments with precision and accuracy.

Lotus (Nelumbo nucifera Gaertn.) is one of China's 10 most renowned commercially grown ornamental flowers. However, lotus is susceptible to various fungal infections, which can affect its rich nutritional composition and ornamental value. In 2023, 12 fungal cultures were isolated from lotus leaves in Liuzhou City, Guangxi Zhuang Autonomous Region, China. Based on the phylogenetic analyses using seven gene primers (ITS, GAPDH, TEF1, Alt a 1, RPB2, EndoPG and OPA10-2) and the phenotypic data, two species (five strains) in the section Alternaria were identified as A. melongenicola L. Zhao & J.X. Deng and A. nelumbiicola T.Y. Zhang. To the data, most of the Alternaria species found, including A. nelumbiicola, have been identified on the basis of host and morphological characteristics and no molecular data were available so far that support these earlier identifications. This study is the first worldwide report of A. melongenicola from lotus. It also provides the gene sequence information of A. nelumbiicola in GenBank, clarifies its phylogenetic position and distinguishes it from the previous classified morphospecies A. caudata which is phylogenetically considered as a synonym of A. alternata. In addition, pathogenicity test confirmed that these two Alternaria species are the causative agents of leaf spot of lotus.

In the absence of alternate hosts, the epidemiology of wheat rusts has remained elusive in many countries including India. This paper synthesises salient information from historical records collected at the Indian Institute of Wheat and Barley Research, from surveys of alternate and alternative hosts, and from observations in off-season wheat disease monitoring nurseries. The primary source of rust inoculum to wheat in the plains of India was previously suggested to originate from off-season wheat, self-sown plants and green bridges in the hills. However, nowadays there is negligible cultivation of off-season wheat in the hills, but still recurrence of wheat rust is a regular phenomenon in India. As observed previously, none of the Berberis spp. or grasses could be related to wheat rusts in this study. Thus, the wheat rust pathogens appear to multiply clonally in India. At present, new virulent pathotypes of Puccinia striiformis f. sp. tritici are identified initially from the plains of Punjab and other areas in the foothills of Himachal Pradesh, and after 6–7 years these pathotypes become predominant in the hills. Moreover, owing to the cultivation of traditional wheat germplasm, less virulent pathotypes of the stripe rust pathogen occur in the higher hills, and most of the present-day wheat varieties are resistant to these pathotypes. The avirulence/virulence profiles of Indian pathotypes of Puccinia spp. on wheat indicate that our pathotype flora is different from that of adjoining countries and continents, which rules out their incursion from the adjoining countries. Wheat rust infection in the plains appears to be independent of the hills and movement of both leaf rust and stripe rust occurs both ways gradually year after year. It appears that the wheat rusts survive on grasses or other non-gramineous hosts occurring locally near water bodies, forests, grasslands or damp areas in the hills and plains of India.

In December 2023, Neolamarckia cadamba trees at Lovely Professional University, Punjab, India, exhibited a necrotic leaf spot disease affecting 20%–40% of their foliage, with similar symptoms observed in nearby farmer fields of Punjab. The disease manifested as concentric ring spots with yellow halos. Samples from three infected leaves of six trees were collected, and tissues from lesion margins were cultured. Six isolates (LPUAE1-LPUAE6) were obtained and cultured on PDA medium, displaying greyish fungal colonies. The conidia were identified morphologically as belonging to the genus Alternaria spp. DNA analysis using ITS and ATPase region amplifications confirmed the isolates as Alternaria ethzedia, with sequences deposited in NCBI GenBank. Till now there is no report on diseases of kadamba caused by A. ethzedia and this first report indicates that further studies on leaf spot disease management are needed.

An investigation of Alternaria blotch in apple orchards was conducted to evaluate the effectiveness of fungicides, plant extracts and bioagents against Alternaria mali through the development of integrated disease management strategies. Under pot conditions, potential combinations of fungicides, plant extracts and biocontrol agents for mitigating the disease were evaluated. Furthermore, polyhouse studies showed that bioagents such as Trichoderma viride, Bacillus subtilis and Pseudomonas fluorescens have disease control potential. Moreover, this research explored the impact of fungicides on the production of primary inoculum, highlighting the potential to impede conidial production by the pathogen. Over two cropping seasons, field assessments indicated the efficiency of integrated approaches in managing Alternaria blotch of apples. This study highlights the importance of reducing chemical inputs while ensuring successful disease management, offering important insights into a more sustainable approach to the management of diseases in apple orchards.

Agriculture is crucial for the economic stability of developing countries, with olive trees in the Mediterranean region providing significant benefits through olive oil and table olive production. However, olive trees are susceptible to diseases that threaten their productivity. Traditional methods of disease detection are time-consuming and impractical on a large scale. Advances in artificial intelligence (AI) and machine learning now offer efficient solutions for rapid and accurate disease identification, which can enhance disease management and increase yields. This study introduces the FLVAEGWO-CNN, an innovative deep learning model designed to improve classification accuracy of olive diseases, particularly in dealing with imbalanced datasets. The model integrates focal loss, variational autoencoders (VAE), grey wolf optimisation (GWO), and convolutional neural networks (CNNs) into a unified framework. The focal loss component addresses class imbalance by assigning more weight to hard-to-classify examples, while the VAE component improves data representation. GWO optimises the CNN's hyperparameters for robust performance. The FLVAEGWO-CNN model was evaluated on a dataset with significant class imbalance, achieving an exceptional accuracy of 99.2% in binary classification and excelling in multiclass classification, particularly in recognising minority classes like ‘Aculus olearius’. The results suggest that this model outperforms existing models and provides a viable solution for imbalanced datasets in classification tasks. To ensure the model's validity, further investigation into potential challenges like overfitting and generalisability is necessary. Future work will focus on validating the model across diverse datasets and refining its architecture. The FLVAEGWO-CNN model sets a new standard for accuracy and reliability in deep learning-based disease classification, with implications for various applications, including medical diagnosis and fraud detection.