Search Results (515)

The fungus Coleosporium zanthoxyli is the causal agent of leaf rust in Chinese prickly ash pepper (Zanthoxylum armatum ‘Hanyuan putaoqing’), seriously impacting its industrial development. However, little is currently known about the infection and pathogenesis of C. zanthoxyli on Z. armatum. In this study, the infection of Z. armatum by C. zanthoxyli was reported at histological and cytological levels by a fluorescence microscope and transmission electron microscopy (TEM) for the first time. Fluorescence microscopy with fluorophore Alexa 488 (WGA-FITC) stained samples revealed that the infection process comprised three distinct stages: penetration (0–1 days post inoculation, dpi), parasitic growth (3–5 dpi), and sporulation (≥7 dpi). The number of haustoria increased during the osmotic and parasitic periods and then decreased; the length of hyphae also increased rapidly and then decreased. TEM analysis during these stages demonstrated that as disease severity increased, chloroplasts and mitochondria enlarged significantly, accompanied by a marked accumulation of starch granules and osmiophilic granules. At later stages, the nuclei became irregular, the grana lamellae were blurred, and the lamellar structure was arranged disorderly, and leaf tissues were extensively colonized by fungal hyphae and haustoria, leading to cellular necrosis and distorted cell walls. Notably, the sporulation phase was characterized by dense rust spore clusters covering the leaf surface. These findings provide critical insights into the ultrastructural changes induced by C. zanthoxyli during infection, elucidating key mechanisms of rust-induced damage in Chinese prickly ash and identifying the parasitic phase as a critical window for control strategies. This study lays a foundation for further research on rust pathogenesis and the development of Chinese prickly ash targeted control strategies. Full article

Yellow rust (YR), caused by Puccinia striiformis f. sp. tritici (Pst), is a global disease infecting wheat that seriously affects the yield and the quality of grains. Wheat breeding line C855 is immune to the mixed Pst isolates CYR32 + CYR33 + CYR34 under field conditions. To identify the Yr-loci carried by C855, in this study, an F₂ population derived from the crossing of C855 with Yannong 999, a YR-sensitive cultivar, was established, and the infection type (IT) of each F₂ individual was estimated. The correlation analysis results show that YR resistance was significantly positively correlated with grain weight and grain size. Using a 120K single-nucleotide polymorphism (SNP) array, the F₂ population was genotyped, and a high-density genetic map covering 21 wheat chromosomes and consisting of 5362 SNP markers was built. Then, five Yr-QTLs on chromosomes 1B, 2A, 2B, and 2D were identified. Of these, the QTL on chromosome 2A, temporarily named QYr.sxau-2A.1, is a major-effect QTL explaining 15.62% of the phenotypic variance. One PCR-based marker SSR2A-14 for QYr.sxau-2A.1 was developed, and the C855 allele of SSR2A-14 corresponded to the stronger Yr resistance. QYr.sxau-2A.1, located in the 228.02~241.58 Mbp physical interval, is different from all the known Yr loci on chromosomes 2A. Within the interval, there are 30 annotated genes, including a nucleotide-binding site and a leucine-rich repeat (NBS-LRR)-encoding gene with the linkage marker NRM2A-16 of QYr.sxau-2A.1. Our results reveal a novel major-effect QYr.sxau-2A.1, which provided resistance to YR and is a molecular marker for wheat breeding. Full article

Fungal pathogens represent a major constraint to global agricultural productivity, causing a wide range of plant diseases that severely affect staple crops such as cereals, legumes, and vegetables. These infections result in substantial yield losses, deterioration of grain and produce quality, and significant economic impacts across the entire agri-food sector. Among phytopathogens, fungi are considered the most destructive, causing a wide range of diseases such as powdery mildew, rusts, fusarium head blight, smut, leaf spot, rots, late blight, and other fungal pathogens. Traditional plant protection methods do not always provide long-term effectiveness and environmental safety, which requires the introduction of innovative approaches to creating sustainable varieties. CRISPR-Cas technology opens up new opportunities for targeted genome editing, allowing the modification or silencing of susceptibility genes and thus increasing plant resistance to fungal infections. This review presents current achievements and prospects for the application of CRISPR-Cas technology to increase the resistance of major agricultural crops to fungal diseases. The implementation of these approaches contributes to the creation of highly productive and resistant varieties, which is crucial for ensuring food security in the context of climate change. Full article

Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (<2%), indicating the universality of spectral patterns of stem rust. Experiments were conducted under controlled laboratory conditions; therefore, direct field transferability may be limited. These findings demonstrate that hyperspectral imaging with robust preprocessing and feature engineering enables early diagnostics of rust diseases in cereal crops. Full article

Despite the recognized social and economic importance of common beans (Phaseolus vulgaris L.), the average grain yield is far below the productive potential of cultivars. This situation is explained by several factors, such as the large number of diseases and pests that affect the crop, some of which cause significant damage. It is estimated that approximately 200 diseases can significantly affect common beans. These can be bacterial, viral, fungal, and nematode-induced. The main bean fungal diseases include anthracnose, angular leaf spot, powdery mildew, gray mold, Fusarium wilt, dry root rot, Pythium root rot, southern blight, white mold, charcoal rot and rust. This review provides a comprehensive overview of eleven major fungal diseases affecting common bean, describing their associated damage, characteristic symptomatology, and the epidemiological factors that favor disease development. It further synthesizes current knowledge on host resistance mechanisms that can be exploited to develop molecularly informed resistant genotypes. The compilation includes characterized resistance genes and mapped quantitative trait loci (QTLs), with details on their chromosomal locations, genetic effects, and potential for use in breeding. Moreover, the review highlights successful applications of molecular breeding approaches targeting fungal resistance. Finally, it discusses conclusions and future perspectives for integrating advanced genetic improvement strategies—such as marker-assisted selection, genomic selection, gene editing, and pyramiding—to enhance durable resistance to fungal pathogens in common bean. This work serves as both a reference for forthcoming resistance-mapping studies and a guide for the strategic selection of resistance loci in breeding programs aimed at developing cultivars with stable and long-lasting fungal resistance. Full article

Barley (Hordeum vulgare L.) is a major fodder crop whose productivity is often reduced by phytopathogens, especially during early growth. Understanding how soil fertility management and microbial communities influence disease outcomes is critical for developing sustainable strategies that reduce fungicide dependence and enhance crop resilience. This study evaluated the resistance of the winter barley cultivar “Zemela” to powdery mildew (Blumeria graminis f. sp. hordei), brown rust (Puccinia hordei), and net blotch (Pyrenophora teres f. maculata). The crop was cultivated under two soil management systems—green manure and conventional—and five fertilisation regimes: mineral, vermicompost, combined, biochar, and control. Phytopathological assessment was integrated with functional predictions of soil microbial communities. Field trials showed high resistance to powdery mildew (RI = 95%) and brown rust (RI = 82.5%), and moderate resistance to net blotch (RI = 60%). While ANOVA indicated no significant treatment effects (p > 0.05), PCA explained 82.3% of the variance, revealing clear clustering of microbial community functions by soil management system and highlighting the strong influence of fertilisation practices on disease-related microbial dynamics. FAPROTAX analysis suggested that organic amendments enhanced antifungal functions, whereas conventional systems were dominated by nitrogen cycling. FUNGuild identified higher saprotrophic and mycorrhizal activity under organic and combined treatments, contrasting with greater pathogen abundance in conventional plots. Overall, results demonstrate that soil fertilisation practices, together with microbial functional diversity, play a central role in disease suppression and crop resilience, supporting sustainable barley production with reduced reliance on chemical inputs. Full article

Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection. Full article

Leaf rust, a devastating fungal disease caused by Puccinia triticina (Pt), severely impacts wheat quality and yield. Identifying genetic loci for wheat leaf rust resistance, developing molecular markers, and breeding resistant varieties is the most environmentally friendly and economical strategy for disease control. This study utilized a recombinant inbred line (RIL) population of Doumai and Shi4185, combined with the wheat 90 K single nucleotide polymorphisms (SNPs) chip data and maximum disease severity (MDS) of leaf rust from four environments, to identify adult plant resistance (APR) loci through linkage mapping. Additionally, kompetitive allele-specific PCR (KASP) markers suitable for breeding were developed, and genetic effects were validated in a natural population. In this study, 5 quantitative trait loci (QTL) on chromosomes 1B (2), 2A and 7B (2) were identified through inclusive composite interval mapping, and named as QLr.lfnu-1BL1, QLr.lfnu-1BL2, QLr.lfnu-2AL, QLr.lfnu-7BL1 and QLr.lfnu-7BL2, respectively, explaining 4.54–8.91% of the phenotypic variances. The resistance alleles of QLr.lfnu-1BL1 and QLr.lfnu-1BL2 originated from Doumai, while the resistance alleles of QLr.lfnu-2AL, QLr.lfnu-7BL1 and QLr.lfnu-7BL2 came from Shi4185. Among these, QLr.lfnu-1BL2, QLr.lfnu-7BL1 and QLr.lfnu-7BL2 overlapped with previously reported loci, whereas QLr.lfnu-1BL1 and QLr.lfnu-2AL are likely to be novel. Two KASP markers, QLr.lfnu-2AL and QLr.lfnu-7BL, were significantly associated with leaf rust resistance in a diverse panel of 150 wheat varieties mainly from China. Totally, 34 potential candidate genes encoded the NLR proteins, receptor-like kinases, signaling kinases and transcription factors were selected as candidate genes for the resistance loci. These findings will provide stable QTL, available breeding KASP markers and candidate genes, and will accelerate the progresses of wheat leaf rust resistance improvement through marker-assisted selection breeding. Full article

HaWRKY33 is induced by salicylic acid and participates in the disease resistance signaling pathway of sunflower rust disease; however, the transcriptional regulatory mechanism of this protein against Sclerotinia sclerotiorum in sunflowers remains unclear. Given this, we conducted a survey of 426 sunflower accessions at the natural disease nursery in Gannan County and identified a single dominant physiological race, MCG1, using simple sequence repeat methods. Additionally, we performed indoor inoculation tests using this dominant race and obtained disease-resistant varieties, W227 and BC2202-03, as well as susceptible varieties, N241 and Z155. Further, we inoculated the above resistant and susceptible combination materials with MCG1 and conducted transcriptomic analysis and RT-qPCR validation. Through KEGG analysis, we found that HaWRKY33 is involved in the plant–pathogen interaction pathway, suggesting that HaWRKY33 may regulate sunflower defense responses against Sclerotinia sclerotiorum through the plant–pathogen interaction pathway. Finally, yeast two-hybrid screening and AI prediction using AlphaFold 3 revealed strong interactions between ARG-189 and GLU-344 amino acids in the HaWRKY33-AOA251SVV7 proteins, indicating that the HaWRKY33-AOA251SVV7 pattern regulates the sunflower defense response against Sclerotinia sclerotiorum in a transcriptional complex form. In summary, these results provide new insights into the disease resistance mechanisms of sunflowers against Sclerotinia sclerotiorum and promote the development of molecular breeding for sunflower resistance to Sclerotinia sclerotiorum. Full article

Stomata play a crucial role in plant immune responses, with their morphological characteristics closely linked to disease resistance. Accurate detection and analysis of stomatal phenotypic parameters are essential for soybean disease resistance research and variety breeding. However, traditional stoma detection methods are challenged by complex backgrounds and leaf vein structures in soybean images. To address these issues, we proposed a Soybean Stoma-YOLO (You Only Look Once) model (SS-YOLO) by incorporating large separable kernel attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8 and Deformable Large Kernel Attention (DLKA) in the Neck part. These architectural modifications enhanced YOLOV8′s ability to extract multi-scale and irregular stomatal features, thus improving detection accuracy. Experimental results showed that SS-YOLO achieved a detection accuracy of 98.7%. SS-YOLO can effectively extract the stomatal features (e.g., length, width, area, and orientation) and calculate related indices (e.g., density, area ratio, variance, and distribution). Across different soybean rust disease stages, the variety Dandou21 (DD21) exhibited less variation in length, width, area, and orientation compared with Fudou9 (FD9) and Huaixian5 (HX5). Furthermore, DD21 demonstrated greater uniformity in stomatal distribution (SEve: 1.02–1.08) and a stable stomatal area ratio (0.06–0.09). The analysis results indicate that DD21 maintained stable stomatal morphology with rust disease resistance. This study demonstrates that SS-YOLO significantly improved stoma detection and provided valuable insights into the relationship between stomatal characteristics and soybean disease resistance, offering a novel approach for breeding and plant disease resistance research. Full article

Wheat, a globally vital food crop, faces severe threats from numerous foliar diseases, which often infect agricultural fields, significantly compromising yield and quality. Rapid and accurate identification of the specific disease is crucial for ensuring food security. Although progress has been made in wheat foliar disease detection using RGB imaging and spectroscopy, most prior studies have focused on identifying the presence of a single disease, without considering the need to operationalize such methods, and it will be necessary to differentiate between multiple diseases. In this study, we systematically investigate the differentiation of three wheat foliar diseases (e.g., powdery mildew, stripe rust, and leaf rust) and evaluate feature selection strategies and machine learning models for disease identification. Based on field experiments conducted from 2017 to 2024 employing artificial inoculation, we established a standardized hyperspectral database of wheat foliar diseases classified by disease severity. Four feature selection methods were employed to extract spectral features prior to classification: continuous wavelet projection algorithm (CWPA), continuous wavelet analysis (CWA), successive projections algorithm (SPA), and Relief-F. The selected features (which are derived by CWPA, CWA, SPA, and Relief-F algorithm) were then used as predictors for three disease-identification machine learning models: random forest (RF), k-nearest neighbors (KNN), and naïve Bayes (BAYES). Results showed that CWPA outperformed other feature selection methods. The combination of CWPA and KNN for discriminating disease-infected (powdery mildew, stripe rust, leaf rust) and healthy leaves by using only two key features (i.e., 668 nm at wavelet scale 5 and 894 nm at wavelet scale 7), achieved an overall accuracy (OA) of 77% and a map-level image classification efficacy (MICE) of 0.63. This combination of feature selection and machine learning model provides an efficient and precise procedure for discriminating between multiple foliar diseases in agricultural fields, thus offering technical support for precision agriculture. Full article

Soybean rust, caused by the fungus Phakopsora pachyrhizi, is recognized as the most devastating disease affecting soybean crops worldwide. In practical applications, performing accurate Phakopsora pachyrhizi segmentation (PPS) is essential for elucidating the morphodynamics of soybean rust, thereby facilitating effective prevention strategies and advancing research on related soybean diseases. Despite its importance, studies focusing on PPS-related datasets and the automatic segmentation of Phakopsora pachyrhizi remain limited. To address this gap, we propose an efficient semantic segmentation model named MS-UNet (Multi-Scale Confusion UNet Network). In the hierarchical Vision Transformer (ViT) module, the feature maps are down-sampled to reduce the lengths of the keys (K) and values (V), thereby minimizing the computational complexity. This design not only lowers the resource demands of the transformer but also enables the network to effectively capture multi-scale and high-resolution features. Additionally, depthwise separable convolutions are employed to compensate for positional information, which alleviates the difficulty the ViT faces in learning robust positional encodings, especially for small datasets. Furthermore, MS-UNet dynamically generates labels for both hard-to-segment and easy-to-segment regions, compelling the network to concentrate on more challenging locations and improving its overall segmentation capability. Compared to the existing state-of-the-art methods, our approach achieves a superior performance in PPS tasks. Full article

Knowledge of the genetic diversity and resistance genes of Coffea canephora genotypes is essential to identify genetic resources that are better adapted to current climate conditions. This study aimed to molecularly characterize and evaluate the genetic diversity of coffee plants cultivated in Rondônia (Amazonia), Brazil, using SNP molecular markers, and to identify plants carrying resistance genes to two major coffee diseases: rust (Hemileia vastatrix) and coffee berry disease (CBD; Colletotrichum kahawae). Genetic diversity analysis revealed five main groups: Group II included 33 genotypes, primarily of the Robusta botanical variety; Group III contained 18 genotypes of the Conilon variety; Group V, the largest, comprised 85 genotypes, mostly hybrids between Robusta and Conilon. Groups I and IV showed fewer, more divergent genotypes. Molecular markers linked to resistance genes enabled the identification of clones with pyramided resistance alleles for both diseases. Three genotypes exhibited a complete pyramided configuration, while others showed different combinations of resistance loci. Marker patterns also allowed classification of genotypes based on origin, variety, and genealogy. These findings provide a valuable foundation for guiding crosses in breeding programs aiming to develop disease-resistant and climate-resilient clones and hybrids, while also supporting cultivar and clone traceability. Full article

Soybean rust, caused by Phakopsora pachyrhizi, is a major foliar disease that often escapes fungicide control, necessitating alternative strategies. We investigated whether phyllosphere arthropods, such as mites and thrips, facilitate the secondary dispersal of the mycoparasitic fungus Simplicillium under controlled conditions. Detached soybean leaves inoculated with P. pachyrhizi were subjected to either arthropod-exposed or arthropod-excluded treatments. Simplicillium isolates were significantly more abundant in the presence of arthropods. Molecular identification revealed identical ITS genotypes of S. lamellicola from both infected pustules and thrips, indicating vector-mediated fungal transmission. While some Simplicillium strains persisted epiphytically without vectors, their spread was minimal. These results highlight a promising approach to enhance the effectiveness of Simplicillium-based biocontrol through natural arthropod-mediated dissemination, warranting field validation of this self-disseminating strategy. Full article

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application. Full article