Journal of Phytopathology最新文献

The majority of the crops are wasted owing to deficiency of transport, plant diseases and lack of storage facilities. Above 15% of crops are worn out in India owing to diseases and therefore it has turned out to be a main concern to be solved. This study introduces an advanced framework for plant disease detection by integrating enhanced image segmentation techniques with robust ensemble classification models. Our methodology begins with the pre-processing of plant leaf images using median filtering and Wiener denoising to reduce noise and enhance image quality. As the next step, the Improved Region Growing Algorithm (IRGA) is deployed for the segmentation of images. Then, features together with ‘Scale Invariant Feature Transform (SIFT), improved Binary Gabor Pattern (IBGP), Haralick features, color features like RGB Color Histogram, disease area and higher order statistical features (Entropy, Skewness, variance and kurtosis)’ are extracted. The improved independent component analysis (IICA) model is then used to choose the best attributes. Lastly, detection takes place using Ensemble classifiers (EC) including Neural Network (NN), modified effective squeeze and excitation block-based deep convolutional neural network (M-ESE-DCNN) and bi-directional gated recurrent unit (BI-GRU). Further, the DCNN weights are optimised via the Colliding Archimedes and Teamwork Algorithm (CA-TWA) model. For the best case with dataset 1, EC + CA-TWA got a high accuracy of 0.94, while EC + BOA, EC + DOX, EC + SSO, EC + TOA and EC + ArOA had lower accuracy. Furthermore, for all schemes, dataset 1 displays superior outputs to dataset 2 and dataset 3. Finally, an evaluation is done to validate this work.

Tobacco brown spot, caused by Alternaria alternata (Fries) Keissler, is one of the main leaf diseases of tobacco. It is common in the mature stage of tobacco but rarely occurs in the early growth stage. Therefore, it is also called ‘mature spot’ by tobacco farmers. However, the physiological and biochemical mechanisms underlying the responses of tobacco plants at different growth stages to A. alternata remain unclear. In this experiment, A. alternata was inoculated into two tobacco varieties, Yun87 and HongHuaDaJinYuan (HD), at the immature and mature stages respectively. The disease index, stomata, reactive oxygen species (OFR and H₂O₂) and main defence enzymes (SOD, POD, PPO, CAT and PAL) of tobacco at the two growth stages were analysed. Results showed that the disease index of tobacco plants in the immature stage was lower than that of tobacco plants at the mature stage at 5 days after inoculation. Stomatal opening was not significantly different between the immature and mature stages, but the stomatal apertures of HD and Yun87 decreased by 1%–2.33% at 12 h after inoculation. Moreover, the OFR and H₂O₂ contents in the mature stage were significantly higher than those in the immature stage. SOD activity was higher in the immature stage than in the mature stage, whereas POD activity was higher in the mature stage than in the immature stage. PPO, CAT and PAL activities were not significantly different between the immature and mature stages. Reactive oxygen species and defence enzymes began to respond at 12 h after inoculation. Specifically, SOD and PAL activities peaked at 12 h, whereas CAT activity peaked at 24 h. The response levels and rates of H₂O₂ content, SOD activity and PAL activity were higher in the immature stage than in the mature stage. Correlation analysis revealed that the strength of the effect of these factors was different. In general, tobacco resistance to brown spot disease was higher in the immature stage than in the mature stage, and this resistance was mediated by the response rates and levels of stomata, reactive oxygen species and defence enzymes.

Plant diseases are the major factors that affects the quality production as it affects or interrupts the plant's vital functions. The early detection of crop disease could assist farmers in implementing the right preventative measures at the right moment to eradicate it. The main goal of the ODHEPDC (Optimal Trained Deep Hybrid Ensemble of Classifier for Classification of Plant Disease) model is the classification of diseases leaf images. The primary step is to improve the input image by using the MF model to remove the noise. This is considered as the preprocessing step. Improved fuzzy clustering algorithm, leading to the identification of the regions, ROI as well as non-ROI. Next to this, the appropriate features are extracted to define the feature set that includes MPPT feature, PHOG feature, and MTP features as well. However, the curse of dimensionality is the greatest crisis in the classification problem, hence, improved feature level fusion is progressed, which is the simple concatenation of the extracted features. In this, the improved calculation of information gain ensures the reduction and fusion of feature set. The fused features are the inputs to ensemble classification model with the classifiers like CNN, RNN, and DBN classifiers, which gives the classified results. To boost up the performance of classification model, the Deep Maxout model in the ensemble is optimally trained by a new Bald Eagle Search Updated Pelican Optimization (BESUPO) Algorithm via optimal weights tuning as the model determines the final classification outcome. The validation results prove the disease classification performance via the given architecture than extant schemes.

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.