First report of Stemphylium xanthosomatis causing leaf spot disease in Polygonatum cyrtonema Hua in China

First report of Diaporthe drenthii causing leaf spot disease of Hedera canariensis in Guizhou, China

3,5-Di-tert-butylphenol from Bacillus siamensis controls kiwifruit leaf spot by disrupting membrane integrity and interfering with energy metabolism of Fusarium graminearum.

First report of Fusarium equiseti and Fusarium oxysporum causing brown leaf spot on Chinese cabbage

Tea plant (Camellia sinensis) originates from China and is one of the most widely consumed beverages in the world. Tea plants are vulnerable to leaf diseases such as Tea Leaf Blight, Tea Red Leaf Spot, and Tea Red Scab, which can reduce the quality and productivity of the harvest. Manual disease identification is still commonly used, but this method has many limitations, such as dependence on farmers’ experience and inaccuracy in early detection. This study aims to apply the YOLOv11 algorithm as an object detection method to automatically, quickly, and accurately detect four classes of tea leaf conditions (three diseases and one healthy). The dataset used consists of 3,960 high-resolution tea leaf images that have undergone segmentation, augmentation, and normalization processes. The research was carried out through image preprocessing, YOLOv11 model training, and model performance evaluation using precision, recall, F1-score, and mean Average Precision (mAP) metrics. The results of tea leaf disease detection using YOLOv11 achieved an average precision of 97.2%, recall of 98.2%, mAP@0.5 of 98.8%, and mAP@0.5:0.95 of 95.5%. This model can be used to help farmers identify tea leaf diseases more quickly and reduce the risk of crop yield losses.

Peanut Leaf Spot Disease Complex: Escalating Threats in the Era of Climate Change

Early and accurate detection of pigeon pea leaf diseases is essential for improving crop productivity and ensuring food security, particularly under real-field agricultural conditions. This paper introduces a shallow and computationally off-the-shelf deep learning system to detect the presence of pigeon pea leaf disease with great accuracy and in real-time on resource-limited cameras. DSLR and smartphone cameras were used to make up a custom high-resolution dataset under natural field conditions, including healthy leaves and major diseases, such as Fusarium wilt, leaf spot, and powdery mildew. All the images were downsampled to 224 × 224 pixels and processed with a Gaussian smoothing filter to remove noise and a Canny edge detector to improve structural features. Disease regions were accurately isolated using a Skill Optimization Algorithm (SOA)-driven segmentation strategy that dynamically optimized threshold levels, morphological kernel sizes, and lesion area constraints to handle background clutter and illumination variations. A pretrained EfficientNet-B0 model was used to extract deep semantic features, which consisted of compact 1280-dimensional feature vectors. A novel FMDDCN approach was used to classify these features through exploiting the sensitivity to subtle disease patterns by relying on differential feature modeling and multi-layer fusion of features. The model was fitted on stochastic gradient descent with a learning rate of 1 x 10-3 and a batch size of 32, and assessed on a 60/20/20 train validation test split with 5-fold cross-validation. The results of the experiment show consistent convergence with low overfitting. The proposed framework was found to produce a classification accuracy of 94.5%, precision of 91.0%, recall of 85.5% and Matthews Correlation Coefficient of 88.5% when it was used with four optimized features. In comparison, it is demonstrated that FMDDCN performs better than traditional machine learning and deep learning models, with its F1-score of 0.965 and the overall accuracy of 0.965. The suitability of the real-time edge deployment is verified, as confirmed by the use of computational analysis to reduce inference latency and memory consumption.

Banana leaf diseases are a significant threat to Cavendish banana production. In the Philippines, the main disease has been diagnosed as Black sigatoka disease caused by Pseudocercospora fijiensis based on symptoms. However, our study showed that the main pathogen in Mindanao island, the largest banana-producing region in the Philippines, belongs to the genus Nigrospora, contradicting previous assumptions. We clarified the phylogenetic positions of 160 Nigrospora isolates based on molecular phylogenetic analyses using ITS, β-tubulin, and tef1α sequences, and compared their morphology with known species. Molecular phylogenetic and morphological analysis revealed that Nigrospora isolates comprised N. chinensis, N. lacticolonia, N. cf. singularis, N. sphaerica, N. vesicularifera, and a novel species, N. nigrocolonia. Pathogenicity tests on banana leaves confirmed that these species are pathogenic. Species other than N. sphaerica were for the first time reported as pathogens of banana leaf. The results of the fungicide sensitivity test using 14 fungicides, including pyrimethanil, spiroxamine, and tebuconazole, for the Sigatoka disease showed 100% inhibition of all isolates at 100ppm of active ingredients. However, low-sensitivity isolates were observed for the remaining 11 fungicides. Our findings indicated the need for a comprehensive review of banana leaf disease prevention strategies.

Abstract In 2024 and 2025, leaf spot symptoms were observed in a jujube ( Ziziphus jujuba ) cultivar collection in Hungary. The symptoms manifested as small, brown necrotic spots, a few millimetres in diameter, surrounded by a chlorotic margin, resembling to Alternaria leaf spot. The pathogen was successfully isolated from symptomatic leaves, and was identified as a member of the Alternaria arborescense species complex through morphological characterization and multilocus phylogenetic analysis targeting the ITS, TEF1-α , and Rpb2 gene regions. Artificial inoculations of detached jujube leaves and seedlings reproduced the characteristic disease symptoms, and subsequent reisolation of the fungus fulfilled Koch’s postulates, confirming its pathogenicity. Hence, this study represents the first report of A. arborescense causing jujube leaf spot. The potential impact of A. arborescens on jujube production and the increasing prevalence of Alternaria leaf spot diseases under the changing climatic conditions, underscore the need for further research.

Leaf spot disease has recently emerged on Euonymus fortunei in Shanxi Province, China, causing noticeable foliar lesions and reducing ornamental value. The causal agent was identified as Alternaria alternata based on morphological characteristics and multilocus phylogenetic analysis using five loci (Alt a1, gapdh, RPB2, ITS, and OPA10-2). Biological characterization showed that mycelial growth was optimal at 27 ℃ on oatmeal agar, whereas conidial germination was favored under alkaline conditions (pH 9.0). The pathogen was sensitive to NaCl stress but showed no significant response to different light regimes. In vitro fungicide sensitivity assays revealed marked differences in inhibitory efficacy between mycelial growth and conidial germination, indicating strong developmental stage-dependent responses. Certain fungicide mixtures exhibited enhanced inhibitory effects compared with single compounds, suggesting potential synergistic interactions. Microscopic observations further revealed fungicide-induced morphological abnormalities, including hyphae swelling, roughened surfaces, reduced sporulation, and deformation of conidial germ tubes. This study represents the first report of A. alternata causing leaf spot disease on E. fortunei in China and provides a basis for understanding pathogen biology and developing effective chemical management strategies for this emerging ornamental disease.

Characterization of five Alternaria species causing leaf spot on broccoli in China

Assessment of farmer knowledge, practices and challenges of cabbage leaf spot disease management in Ashanti region of Ghana

First report of leaf spot of box elder (Acer negundo) caused by Neopestalotiopsis scalabiensis in China

Evaluating Medicago spp. plant genetic resources for resistance to spring black stem and leaf spot pathogen

First report of leaf spot on mango caused by Corynespora cassiicola in China

Bioactivity and mechanisms of Ewingella americana for the control of Alternaria leaf spot on peanut

Enterobacter species affect a wide range of plant hosts. They cause a range of symptoms including leaf spots and blight, wilt and root diseases, decay and soft rot and cankers. Some Enterobacter species include strains that are plant growth promoters and occur either in the rhizosphere or as endophytes. Additionally, some strains can protect their hosts from pathogen attack and are regarded as promising biological control agents. Some strains also have potential for the bioremediation of various compounds. Information on the pathogenicity and virulence mechanisms of plant-pathogenic Enterobacter species is limited. Comparison of diverse genomic features revealed no overall differences between plant-pathogenic and plant-beneficial strains. While often reported as a plant pathogen, there is currently no evidence that Enterobacter is the primary cause of any of the reported diseases. In many cases, they would rather act opportunistically. This remains a significant concern, as a wide range of hosts are affected, and problems may intensify due to global warming. It is crucial to investigate these strains for plant pathogenicity and evaluate the risks to human health.

Accurate identification of apple leaf diseases in field conditions is essential for sustaining crop yield and supporting precision agriculture. Variable illumination, cluttered backgrounds, and co-occurring symptoms complicate diagnosis in real orchards. This study applies a deep learning approach using a fine-tuned MobileNetV2 model to classify apple leaf diseases from a heterogeneous dataset derived from the Plant Pathology 2021 (FGVC8) benchmark. The original five labels were expanded by subdividing the "multiple disease" category into expert-defined compound subclasses, yielding 12 disease categories encompassing both single and compound infections. Data augmentation and transfer learning were employed to improve robustness, while interpretability was assessed through Grad-CAM and LIME visualizations. Results show that the model performs well on distinct single-disease categories such as rust, scab, and frogeye leaf spot, but struggles to detect overlapping or compound infections. These findings highlight both the potential and the challenges of lightweight CNN architectures for agricultural image classification. The study contributes evidence that explainable, compact deep learning models can support future efforts to build reliable tools for plant health monitoring in diverse field conditions.

The production of Phaseolus vulgaris L., an important legume cultivated in Türkiye, is negatively affected by yield losses caused by diseases such as anthracnose (Colletotrichum lindemuthianum (Sacc. & Magnus) Lambs. Scrib.) and rust (Uromyces appendiculatus (Pers.: Pers.)). In this study, the effects of foliar applications of a commercial fungicide containing 80% maneb, 16% manganese, and 4% zinc at different doses (0.5, 1, 2, 4, and 8 g L⁻¹) on the morphology and anatomy of root, stem, and leaf tissues of common bean were investigated. The results showed that high-dose fungicides caused leaf smut, spotting, and curling, as well as a reduction in leaf number and size. While no statistically significant change in root length was observed, stem length decreased significantly at higher fungicide doses. When anatomical data analyses were evaluated, that increasing fungicide concentrations led to significant structural changes in root, stem, and leaf tissues. The findings highlight the importance of appropriate management of fungicide doses by demonstrating that high-dose fungicide applications can cause adverse morphological and anatomical effects on bean plants.

Cercospora leaf spot, a fungal disease caused by Cercospora canescens Ellis and Martin, that occurs frequently and is a more prevalent and destructive disease, causes huge losses to crop production in Mothbean (Vigna aconitifolia) growing region. The experiments were conducted to investigate the bio-efficacy of fungicides against cercospora leaf spot disease of mothbean during Kharif, 2020 and 2021. Collective findings of the two subsequent years (2020 & 2021) of investigation indicated that the minimum percent disease intensity (7.6%) of cercospora leaf spot caused by C. canescens Ellis and Martin, was recorded in two foliar sprays with pyraclostrobin 133 g/l + epoxiconazole 50 g/l SE @ 1.5 ml/l along with maximum seed yield (438 kg/ha), Rs. 10459 net return, and a 1.76 benefit-cost ratio. To a great extent, difenoconazole 25% EC @ 0.5 ml/l also reduced the disease menace and was found to be the second-best-performing fungicide against the targeted disease in mothbean. The findings indicated that after the 10th day of the second spray, 9.4 percent disease intensity and 414 kg/h seed yield with Rs. 9407 net return and a 1.69 benefit-cost ratio were recorded in the difenoconazole 25% EC @ 0.5 ml/l treatment. Absorbingly, the maximum cercospora leaf spot (31.0%) was recorded in the control along with minimum (260 kg/h) seed yield, Rs. 2930 net return, and 1.25 benefit- cost ratio. Therefore, the application of pyraclostrobin 133 g/l + epoxiconazole 50 g/l SE @ 1.5 ml/l is most feasible treatment for the management of cercospora leaf spot (C. canescens) perils in mothbean crop.