Journal of Phytopathology最新文献

Hot pepper is a vital crop in Ethiopia and worldwide, valued for its pungency and as a source of income for stallholder farmers. However, production is severely impacted by Fusarium wilt, leading to yield losses ranging from 10% to 80% and significant economic damage, estimated at $65.3 billion globally. In Ethiopia, Fusarium wilt is widespread, with an incidence rate of 86.6% and yield losses reaching up to 80%. The pathogen blooms in warm, humid conditions, with optimal growth at 25°C and pH 7.0. It infects plants through roots, colonising the xylem and leading to wilting and plant death. The disease spreads through contaminated soil, water, farming tools, and infected seeds, making it difficult to control. Timely and precise diagnosis using morphological features (such as colony colour, shape, and spore structure) and molecular techniques (like PCR targeting and sequencing EF-1α and rpb2 genes, and additional genetic markers acl1, tub2, CaM, and rpb1) are essential to design effective management options. The pathogen's virulence is driven by toxin production, cell wall-degrading enzymes, and effector proteins that suppress plant immunity. Management includes cultural practices, biocontrol, and fungicides, though resistance is a concern. Integrated disease management (IDM), combining these approaches, offers sustainable control of Fusarium wilt. Modern biotechnological tools such as gene editing (CRISPR-Cas9) and RNA interference (RNAi) offer promising solutions but remain underutilised. Although there are identified resistant genotypes, adoption remains limited. This review emphasises the importance of integrated management and multi-omics approaches to improve resistance breeding, along with advocating for farmer education and policy support to reduce crop losses.

Bai-ji (Bletilla striata), an economically important medicinal herb cultivated in Yunnan Province, China, was observed with severe anthracnose symptoms in July 2021. Through comprehensive morphological characterisation and multilocus phylogenetic analysis (ITS, ACT, TUB, and GAPDH gene regions), the causal agent was identified as Colletotrichum cymbidiicola. Initial symptoms manifested as brown necrotic lesions that progressively developed into elliptical or fusiform spots with light brown centers. Koch's postulates confirmed that C. cymbidiicola was the etiological agent. This study is the first documented case of C. cymbidiicola infecting B. striata in China, providing valuable phytopathological insights for the sustainable cultivation of this medicinal species.

This study reports Neofusicoccum parvum for the first time in grapevine planting material in Montenegro, alongside the first detection of the endophytic fungus Trichoderma citrinoviride in the country. Recognising the critical role of healthy planting material in managing Grapevine Trunk Diseases (GTDs), asymptomatic grafted vines from a domestic nursery and imported sources were analysed. Fungal isolates were recovered from woody tissues and identified based on morphological characteristics and molecular sequencing of the ITS, TEF1-α, TUB2, LSU and RPB2 genes. Neofusicoccum parvum was the sole GTD-associated pathogen detected, confirming that planting material, particularly imports, represents a major pathway for disease introduction. Considering the analysis of planting material from multiple countries included in this study, these findings have significant phytosanitary implications for both Montenegrin viticulture and viticulture of the Balkan. Trichoderma citrinoviride, isolated from Vitis vinifera cv. Vranac in Montenegro, was evaluated both in vitro and in planta for antagonistic activity against N. parvum, showing promising inhibitory effects. These results highlight the urgent need for strict nursery sanitation and phytosanitary monitoring and provide a basis for further evaluation of T. citrinoviride as a potential biocontrol agent in sustainable GTD management.

Rice brown spot (RBS), mainly caused by Bipolaris oryzae, is an emerging threat to rice production across Africa, particularly within lowland rice ecosystems that dominate the region. Despite its growing impact, knowledge of the environmental factors influencing RBS occurrence remains limited. This study aimed to elucidate the influence of meteorological variables on RBS prevalence in Côte d'Ivoire, spanning three major agro-ecological zones. Field surveys were conducted in 99 farmer-managed fields during both the rainy and dry seasons from 2021 to 2022, and average RBS severity was calculated across different stands within each field. Parameters assessed included air temperature, relative humidity, wind speed, soil moisture, solar radiation, and rainfall. Linear regression analyses were used to identify main drivers, and regression models were developed to project future risk maps under SSP2-4.5 (moderate) and SSP5-8.5 (high) climate scenarios. Key drivers were identified: air temperatures above 27°C and wind speeds exceeding 1 m/s were associated with decreased disease prevalence, whereas relative humidity above 80%, wet bulb and dew point temperatures between 23°C and 27°C, and soil moisture and wetness in the range of 0.4–0.7 significantly promoted RBS development. Results revealed a yearly increase in RBS severity, ranging from 2.47 to 3.46 (on a 0–5 scale), with consistently higher prevalence during the rainy seasons. Predictive modelling based on these variables suggests a likely expansion and intensification of RBS risk for the future. Under a high-emission scenario, RBS severity could reach 4.9 by the late 21st century, while the moderate scenario projects a peak severity of 4.33 between 2060 and 2080. These findings underscore the pivotal role of climate in shaping RBS risk and highlight the urgent need for climate-adaptive disease management strategies to sustain rice production in Africa.

In August 2024, a severe leaf spot disease was observed on white sapote (Casimiroa edulis), a promising specialty crop in the Rutaceae family, in China. The causal agent was isolated and identified as Corynespora cassiicola based on the morphological characteristics and a multilocus phylogenetic analysis of the ITS, tef1-α and rpb2 gene regions. Pathogenicity was confirmed through Koch's postulates. To our knowledge, this is the first record of C. cassiicola causing leaf spot disease on C. edulis in China and represents the first record globally of its natural infection on a Rutaceae host. This study provides a crucial foundation for disease control and further research into the epidemiology and evolution of this pathogen.

Early detection of fruit plant diseases and timely intervention pose significant challenges for farmers. Leaf diseases caused by fungi and viruses, including apple scab, Marconian coronary, black rot canker, powdery mildew, apple mosaic and Alternaria leaf spot, severely impact crop yield. This study proposes a modified optimal mobile network-based convolutional neural network (OMNCNN) for effective detection of fruit plant leaf diseases. An IoT Smart Monitoring System is developed to track environmental factors such as light intensity, temperature and soil moisture using sensor networks combined with cloud-based real-time data communication over wireless networks. Input images undergo contrast enhancement and histogram equalisation to suppress distortions and highlight key visual features. Dimensionality reduction is achieved via singular value decomposition (SVD), enabling accurate disease diagnosis. The OMNCNN model automatically classifies healthy and diseased leaves with high precision. Fertiliser recommendations are generated based on soil type and nutrient levels detected by an NPK sensor, considering soil and plant-specific nutrient demands. Also, a stacked autoencoder-based recommendation system provides farmers with insights into soil conditions, weather and disease prevention strategies, supporting improved crop management and yield maximisation. Experimental results demonstrate superior performance with a maximum accuracy of 99.8%, precision of 99.3%, recall of 99.1% and F-score of 99.1%, outperforming recent state-of-the-art methods. Disease severity is also estimated efficiently through an optimised model with reduced computation time. This integrated approach, combining mutation-based Henry Gas Solubility Optimisation with deep learning and IoT-enabled environmental monitoring, offers significant improvements in disease detection accuracy, recommendation quality and computational efficiency for smart agriculture applications.

Pseudomonas syringae pv. atrofaciens, a seed-borne pathogen causing basal glume rot, leads to significant wheat yield losses. While this pathogen has been reported in several countries, its presence in Türkiye remains undocumented. This study aimed to isolate and identify P. syringae pv. atrofaciens from wheat seed samples collected during the 2022–2023 harvest season in Yozgat Province, Türkiye. A total of 200 wheat seed lots were collected, of which 125 lots (62.5%) showed signs of infection, including black discoloration at the basal end of the grains. Bacterial isolation on King's B medium led to the purification of the 55 green-blue fluorescent colonies, with 17 isolates confirmed as pathogenic through bioassay tests on wheat leaves. Molecular identification was first performed using MALDI-TOF mass spectrometry, followed by 16S rDNA and rpoD gene sequencing. While 16S rDNA provided reliable genus-level identification of the isolates as P. syringae, rpoD gene analysis enabled precise identification at the pathovar level as P. syringae pv. atrofaciens, showing high similarity to reference strains. Genetic diversity within the population was assessed using BOX-PCR, revealing two distinct genetic profiles. This study provides the first report of P. syringae pv. atrofaciens in wheat seed samples from Turkish agricultural fields, highlighting the importance of improved seed hygiene practices and effective management strategies to mitigate yield losses associated with P. syringae pv. atrofaciens.