Journal of Phytopathology最新文献

Seed samples of rice were collected from north India during two consecutive crop seasons, 2022 and 2023. Isolations of fungi associated with discoloured seed samples were performed and various species of Aspergillus obtained from 1966 samples (the large majority from the state of Punjab) were characterised morphologically for their identification. A total of 109 isolates of six different species of Aspergillus were obtained from 22 popular rice varieties. Species level identification was confirmed by using ITS and β tubulin gene loci. A. flavus and A. niger were the most prevalent species having frequencies of occurrence of 33.0% and 29.9% respectively. Approximately 8.3% of the isolates were predicted to be positive for production of aflatoxins and 1.8% of the isolates were predicted positive for production of ochratoxin based on PCR screening. Quantitative analysis of toxin production by selected isolates using liquid chromatographic tandem mass spectrometry (LC–MS/MS) revealed that isolate Af22 of A. flavus obtained from rice variety PR 126 produced 20.7 μg/kg aflatoxin B1 and 28.7 μg/kg aflatoxin B2 and isolate Ao5 of A. ochraceus obtained from variety PR 121 produced ochratoxin A (8.6 μg/kg) were the most toxigenic isolates. Pathogenicity tests on susceptible rice variety PR 114 demonstrated that all toxigenic isolates induced distinct discoloration patterns on rice grains after an incubation period of 5–7 days. The study underscores the importance of continuous monitoring of Aspergillus contamination and calls for stringent post-harvest management practices to reduce the risk of mycotoxin contamination in rice.

Plant diseases are considered one of the most serious problems in world agricultural production. Regular monitoring and detection are essential to control plant diseases, and effective management methods are used to prevent disease spread and lower pesticide costs. Smart agriculture techniques are one of the key solutions in plant disease prediction and improving crop productivity. Even though various papers have been published on the model for plant disease prediction based on smart agriculture, there is still a lack of an overall systematic model. The proposed approach has been developed to overcome the challenges faced by the existing method. This presented approach uses deep learning and meta-heuristic techniques to detect and classify crop diseases, providing an accurate and efficient solution for farmers to improve crop yield. The process begins with collecting crop disease images from the Kaggle database. Initially, noise removal and contrast enhancement are performed using a Gaussian Amended Wiener Filter (GAWF). Next, the Modified Residual U-Net (MRU-Net) model extracts significant disease regions from the images. Effective features are collected from these segments using a convolutional neural network (CNN) and an improved vision transformer model (IViT). Finally, classification is performed with a stacking ensemble model that incorporates XGBoost (XGB), Gradient Boosting (GB) and AdaBoost-Decision Tree (AdB-DT). The proposed model achieved an accuracy of 99.74% on the PlantVillage dataset, 99.51% on the PlantDoc dataset and 99.57% on the Pigeonpea Leaf Disease dataset, demonstrating its robustness and generalizability across both curated and real-world agricultural image conditions. Also, the proposed approach provided insights into disease identification by utilising Grad-CAM to provide visual explanations.

Divergent biotic stresses adulterate tomato fruit and degrade its quality in maturation, harvesting, and in preservation phases. Mycotic infections are attributable to a substantial decline in the quantity and quality of fruits. This study focused on the prognosis and control of post-harvest fruit rot of tomato. Infected fruits were collected, and the etiological agent was recognised as Aspergillus fumigatus by employing microscopic, morphological and molecular analyses. To prevent fruit rot disease of tomato, Fe₂O₃ nanoparticle (NPs) were synthesised by utilising fruit extract of Terminalia chebula . UV–vis spectroscopy depicted the absorption peak at 268 nm corroborating the nanoscale formation of Fe₂O₃ NPs. Fourier transform infrared indicated the presence of organic compounds (amines, phenol, ester, sulfoxide, aldehyde, alkane and alcohol) on nanoparticles. X-ray diffraction analysis delineated the average size of 29.35 nm and crystalline nature of Fe₂O₃ nanoparticles. Scanning electron microscopy provided insight into the cube-shaped morphology of nanoparticles, and energy dispersive X-ray demonstrated the existence of Fe and O peaks. These Fe₂O₃ NPs manifested a substantial suppression of mycelial growth both in vivo and in vitro. Among all concentrations, 1.0 mg/mL concentration of Fe₂O₃ NPs exhibited the highest efficacy, suppressing mycelial growth by 88.54% in vitro. At the same concentration, Fe₂O₃ NPs markedly suppressed the progression of tomato fruit rot (76%), in vivo. At 1.0 mg/mL concentration of Fe₂O₃ NPs, tomato fruit demonstrated a high amount of titratable acidity, reducing sugars, total sugars, total soluble solids, ascorbic acid, lycopene, and maintained fruit firmness. Our findings indicate that Fe₂O₃ NPs synthesised from fruit extract of T. chebula are effective in controlling fruit rot, prolonging shelf life, and maintaining the fruit quality. These nanoparticles are environmentally sound and efficacious substitutes for chemical fungicide.

The biochemical responses of two mung bean (Vigna radiata L.) cultivars to charcoal rot disease, caused by the fungal pathogen Macrophomina phaseolina, were investigated under controlled greenhouse conditions. The cultivars were Bireshwar (WBM 4-34-1-1), which is resistant to the disease, and Samrat (PDM-139), which is susceptible. The greenhouse environment was maintained at a temperature of 24°C ± 2°C and a relative humidity of 85%–90%. These cultivars were initially identified for their varying resistance levels through field screening. Seeds were treated with four distinct defence-inducing compounds: salicylic acid (SA), chitosan, yeast extract and jasmonic acid (JA). These elicitors were applied at three concentrations: SA (0.5, 1, 2 mM), chitosan (0.01, 0.03, 0.07 mM), yeast extract (0.02%, 0.05%, 0.1%) and JA (0.02%, 0.05%, 0.1%). The treated plants exhibited a notable and statistically significant enhancement in the accumulation of several defence-related biochemical markers, including catalase (CAT), peroxidase (POD), polyphenol oxidase (PPO), phenylalanine ammonia lyase (PAL), ortho-dihydroxy phenols (OD phenols), total phenols and ascorbic acid, when compared to the untreated control group. SA at higher concentrations was most effective, followed by chitosan and JA, in enhancing these biochemical defences. Following pathogen inoculation, elevated levels of CAT, PAL, POD, PPO, OD phenols and total phenols were associated with reduced disease severity. Correlation analysis revealed a strong negative relationship between Percent Disease Index (PDI) and most biochemical variables, especially at 20 days after sowing (DAS), except for PPO and ascorbic acid. These findings underscore the critical role of biochemical defence mechanisms in conferring resistance to charcoal rot disease. The study suggests that high levels of biochemical activity during the early stages of plant development could be used as a valuable selection criterion in breeding programmes aimed at enhancing disease resistance.

Karnal bunt of wheat (Triticum aestivum L.), caused by Tilletia indica, poses a persistent threat to grain quality, seed trade and production stability. The present study was undertaken to assess the spatial distribution of Karnal bunt over two consecutive Rabi seasons (2021–22 and 2022–23) and identify resistant genotypes suitable for cultivation in the north-western Himalayan region. Hundred genotypes were evaluated under artificially induced epiphytotic field conditions. Of these, four genotypes (IC416075, IC145334, IC572925 and IC279616) exhibited complete resistance, recording 0.00% disease incidence and coefficient of infection. Forty-two genotypes were resistant, forty-six were moderately susceptible, six susceptible and two highly susceptible. Genotype × environment (G × E) interaction analysis, including AMMI and stability regression models, identified HD-1105, PBW-803 and PBW-824 as stable performers across environments. Hierarchical clustering and Mahalanobis distance analysis further highlighted substantial genetic divergence among the test genotypes. The findings underscore the significance of integrating field surveillance with resistance screening to identify stable, disease-resistant genotypes for effective management of Karnal bunt in vulnerable wheat-growing regions.

In Malaysia, four species of Colletotrichum, namely C. fioriniae, C. scovillei, C. fructicola and C. truncatum, have been identified as the causal pathogens of chilli anthracnose. In this study, we assessed the potential of cross infection of C. fioriniae, C. scovillei, C. fructicola and C. truncatum on banana, mango and tomato fruits. The most virulent species seems to be isolates of C. fioriniae, and tomato was the most susceptible host. The results of the pathogenicity test showed the risk of cross-infection of the four species of Colletotrichum from chilli fruits with bananas, mangoes and tomatoes. The presence of wounds on fruits enhances the pathogenic potential of Colletotrichum. Identifying the host range of Colletotrichum spp. is essential for developing disease management strategies and efficient quarantine protocols.