Journal of Phytopathology最新文献

The objective of this study was to identify the causal agent responsible for uncommon rot symptoms observed in chilli pepper fruits in Brazil. Symptomatic fruits exhibiting dry rot were collected from fields in the municipalities of Chã Grande, Primavera and Vitória de Santo Antão, Pernambuco state, Brazil. Fourteen fungal isolates were obtained from these samples and identified as belonging to the genus Fusarium through morphological comparisons. DNA was extracted from all 14 isolates, and partial sequences of the translation elongation factor 1-alpha (TEF1-α) and the second largest subunit of RNA polymerase (RPB2) were amplified and sequenced for species identification. BLAST analysis against the FUSARIUM-ID and NCBI databases revealed identity matches ranging from 99.89% to 100% with the Fusarium incarnatum-equiseti species complex (FIESC). The phylogenetic tree, constructed using Bayesian inference based on the concatenated TEF1-α and RPB2 sequences, confirmed that the isolates obtained in this study belong to the FIESC. More specifically, isolates LPPC28, LPPC29, LPPC30, LPPC32 and LPPC34 clustered with F. pernambucanum, while LPPC35, LPPC41 and LPPC44 grouped with F. citri. Isolates LPPC50 and LPPC52 were associated with F. arcuatisporum, whereas LPPC54, LPPC45 and LPPC53 clustered with F. sulawesiense, F. caatingaense and F. hainanense, respectively. All isolates induced dry rot symptoms when inoculated into chilli pepper fruits. To the best of our knowledge, this is the first report of F. pernambucanum, F. caatingaense, F. arcuatisporum, F. sulawesiense, F. citri and F. hainanense as causal agents of dry rot in chilli pepper fruits. These findings contribute to a better understanding of Fusarium diversity in chilli pepper and may assist in developing targeted disease management strategies aimed at reducing field and postharvest losses.

Anthracnose symptoms were observed on water speedwell (Veronica anagallis-aquatica ) plants in Gangwon and Gyeonggi provinces, Korea, during field surveys conducted in May 2024 and 2025, affecting approximately 80% of the surveyed plants. The typical symptoms exhibited dark brown to black necrotic lesions that developed on the leaves and stems. A fungus was isolated from the collected diseased samples, and two representative isolates (KACC 410829 and KACC 411071) were identified as Colletotrichum tabacum on the basis of morphology and multi-locus phylogenetic analyses of ITS, GAPDH, CHS-1, HIS3, and ACT sequences. Pathogenicity tests reproduced the symptoms on healthy leaves, and the same fungus was reisolated, fulfilling Koch's postulates. This is the first report of C. tabacum infecting water speedwell plants, expanding its known host range and highlighting its potential threat to aquatic and wetland vegetation in Korea.

Fungal contamination poses a significant threat to the quality and shelf life of fresh produce, particularly during post-harvest storage and transport, resulting in substantial economic losses. Although synthetic fungicides remain the primary means of fungal control, their environmental persistence and potential health risks underscore the urgent need for safer natural alternatives. In this study, the chemical composition of Ruta montana essential oil (EO) was determined using gas chromatography–mass spectrometry (GC–MS). Its antifungal activity against the major post-harvest fungal pathogens of strawberries (Geotrichum candidum, Rhizopus stolonifer and Botrytis cinerea) was assessed using broth macro-dilution and poisoned medium assays. The synergistic potential of R. montana EO in combination with an aqueous saffron (Crocus sativus L.) extract enriched in crocins was further evaluated via the agar well diffusion method and confirmed through in vivo application on strawberries. The dominant compounds of the R. montana EO were 10-Nonadecanone (51.55%), Psoralen, (12.2%), and 2-Nonanone (4.66%). Strong antifungal activity was observed, with the EO-crocin extract mixture exhibiting enhanced efficacy and pronounced synergistic interactions. These results highlight the potential of R. montana essential oil, particularly when combined with saffron-derived crocins, as an eco-friendly and effective alternative to synthetic fungicides for managing post-harvest fungal infections in the food industry.

One of the most widely grown cultivated natural fibre crops is cotton in the world, but it is extremely susceptible to diverse diseases and pests, which significantly minimise harvests and quality. Existing cotton disease detection system offers challenges due to noisy images and limited feature generalisation, which leads to minimise detection accuracy and real-time feasibility. To address these problems, this paper proposes an Internet of Things (IoT) enabled deep learning (DL) model for automatic cotton plant disease detection and classification. A Raspberry Pi system was used to collect the inputs in the proposed study. The proposed method uses a hybrid attention-enabled residual neural network (HARNN) model for cotton plant disease classification. Initially, the input images are pre-processed with adaptive weighted mean filtering (AWMF) to remove unwanted noise. Handcrafted features are extracted using a histogram of oriented gradients (HOG), a uniform local binary pattern (uLBP) and a grey level co-occurrence matrix (GLCM); statistical features are extracted using mean, standard deviation, skewness, kurtosis, minimum and maximum value and deep features are extracted using the Inception V3 model. The extracted features are fused using the Fusenet model. The concatenated feature is fed into the proposed HARNN classifier to classify varied types of cotton plant disease. HARNN model's parameters are fine-tuned with the chaotic Lyre Bird (CLBOA) optimization method. The simulation results show that the proposed research obtained superior results in terms of accuracy of 99.7%, precision of 99.3%, recall of 99.5%, f-score of 99.8% and specificity of 99.9%, respectively.

Sheath blight disease is one of the most severe diseases affecting rice cultivation worldwide. In addition to being a fungal disease, it is a battle for the future of global food security. Host pathogen interactions in the Rhizoctonia solani–rice system remain a critical but interesting area of study. To elucidate the dynamics underlying this pathosystem, a characterised virulent isolate of R. solani and seven native rice varieties (Jyoti, Zenith, Tetep, Swarna, KMP220, MTU1010 and BR2655) were used in the current study. Inoculation of rice varieties with the pathogen resulted in differential disease reactions both in vivo and in vitro. The variety ‘Tetep’ presented the shortest lesion length (17.28%), followed by the moderately resistant varieties Zenith and KMP220, while the highest susceptibility was found in the variety Jyoti (68.30%), followed by the variety Swarna, and moderate susceptibility was detected in the varieties MTU1010 and BR2655 under glasshouse conditions. A leaf detachment assay revealed 83.29% less lesion area in the Tetep variety than in the Jyoti variety at 5 days post-inoculation (dpi). Further study of the biochemical profiles of defence enzymes, that is, polyphenol oxidase (PPO), peroxidase (POD), superoxide dismutase (SOD) and phenylalanine ammonia lyase (PAL), in these varieties revealed significantly greater induction of defence enzymes in resistant varieties. Scanning electron microscopy (SEM) revealed differential anchorage and penetration along with critical structures such as abundant cuticular wax deposition, linearly arranged papillae and stomatal closing in the resistant variety ‘Tetep’, which hinder pathogen establishment. This study highlights the marked differentiation of pathogens toward different rice varieties and their biochemical responses, which can be utilised as targets for genetic improvement for the sustainable management of disease.

Root-knot nematodes (RKNs; Meloidogyne spp.) are polyphagous pests affecting a wide range of crops in tropical and subtropical regions, causing substantial annual yield losses. Traditionally, Meloidogyne spp. are identified microscopically based on perineal pattern morphology. This method presents challenges due to the visual similarity of these patterns and its dependence on the availability of experts, making the identification process slow and ineffective. This study employed a deep learning approach to overcome challenges associated with manual identification and to enhance the accuracy of RKN species identification based on perineal patterns. Six common species were studied for their identification. A YOLOv8 model was trained using a small sample dataset to identify the region of interest in microscopic images, minimising noise and allowing the model to focus on the necessary features during training. This procedure also increased the number of images. Subsequently, ResNet50, ResNet101, EfficientNetB0, EfficientNetB1 and EfficientNetV2S deep learning models were trained and evaluated to determine the best model for this classification task. EfficientNetB0 outperformed others in terms of average precision, average recall, and average F1 score of 98.0%, 98.1% and 98.1%, respectively. Beyond its high accuracy, EfficientNetB0 offers significant advantages in its smaller model size (16.4 MB) and faster inference time (0.0553 s), making it highly efficient. These results could facilitate the development of a web-based application for the global identification of these significant agricultural pests.

Viral diseases are common in yams (Dioscorea spp.) across Africa, caused by a diverse range of pathogens that can significantly reduce yields. Although yams in Uganda frequently display virus-like symptoms, the specific causal agents have remained largely uncharacterised. This study aimed to identify viruses infecting yams in Uganda and assess their diversity. Forty yam leaf samples—pooled from 200 symptomatic and asymptomatic plants collected from 40 fields across four central Ugandan districts—were tested for viruses. Twelve of the pooled samples were analysed by high-throughput sequencing (HTS) to identify viral sequences, while the remaining 28 were screened using conventional reverse transcription polymerase chain reaction (RT-PCR) with virus-specific primers. Three viruses were detected: yam mosaic virus (YMV) and yam mild mosaic virus (YMMV) from the genus Potyvirus, and Chinese yam necrotic mosaic virus (CYNMV) from the genus Macluravirus. Mixed infections involving up to two viral pathogens were common. Sequence analyses revealed close genetic relationships between the Ugandan isolates and those reported from Ivory Coast (YMV), Brazil (YMMV) and Japan (CYNMV). This study documents the occurrence of multiple yam viruses in Uganda for the first time, including the inaugural report of CYNMV in the country. The findings provide a critical baseline for virus surveillance, inform clean seed production systems and support the development of breeding strategies for resistance.

Ultraviolet-B (UV-B) irradiation was evaluated as a non-chemical strategy for suppressing black spot disease in roses (Rosa × hybrida) caused by Diplocarpon rosae. Daily 1-h night-time UV-B irradiation significantly reduced fungal colony growth on potato dextrose agar, resulting in colony diameters that were 20.4%–37.7% of the non-UV control after 35 days. In detached leaf assays, lesions produced by night-irradiated conidia were markedly smaller, with lesion size reduced to approximately 59.6%–64.8% of the non-UV control. When leaves were pre-treated with UV-B prior to inoculation with untreated conidia, lesion development was suppressed to a lesser but significant extent. Day-UV and night-UV pre-treatments reduced lesion diametre from 10.17 mm (non-UV) to 7.55 mm (25.7% reduction) and 8.32 mm (18.1% reduction) after 21 days, respectively, indicating modest UV-B-induced resistance. Cyclobutane pyrimidine dimers (CPDs)—the major DNA lesions formed when UV-B photons induce covalent bonding between adjacent pyrimidine bases—were quantified using ELISA in D. rosae. CPD accumulation increased in accordance with UV-B dose, with the highest levels detected under night-UV irradiation (relative CPD level = 1.78) compared with day-UV (1.30) and dawn-UV (1.29). Because CPDs distort the DNA helix and inhibit replication, their accumulation under limited photorepair conditions (i.e., at night) likely contributes to fungal growth suppression. Overall, these findings demonstrate that night-time UV-B irradiation suppresses black spot disease through direct CPD-mediated inhibition of D. rosae, with additional but modest contributions from UV-B-induced resistance in rose leaves. Importantly, the UV-B doses used caused no detectable phytotoxic effects on the host tissue.

Obligate oomycete, Albugo candida is a serious pathogen causing white rust disease of Indian mustard as well as several other hosts in the Brassicaceae family. In India, significant progress has been made in dissecting the genetic basis of disease resistance in Brassica juncea against A. candida. However, parallel progress in understanding genomic features and evolution of A. candida infecting B. juncea is relatively lacking. Hence, we here report the draft genome of A. candida isolate AC-BPM infecting B. juncea, which is the leading oilseed crop in the country. The draft genome is 36.57 Mb with a GC content of 42.21% and BUSCO completeness of 99%. AC-BPM secretome was found to be enriched in protein families such as glycosyl hydrolases, elicitins, transglutaminase elicitors, peptidases, cellulases, lipases, etc. No RxLR and CRN class of effectors were found in A. candida AC-BPM secretome. However, we identified CCG class of effectors in A. candida AC-BPM that have homology to earlier known CCGs having evidence of recognition by WRR4 paralogues and initiating immune response. Phylogenetic analysis based on whole genome sequence and single copy orthologous genes placed A. candida AC-BPM in close proximity to B. juncea infecting race 2 (Ac2V) of A. candida, thus highlighting the host-specific adaptive evolution within A. candida. The genomic information obtained in this study could serve as a useful resource for further investigating mechanism of virulence, ecology and evolution of A. candida infecting B. juncea.