Search Results (185)

A total of 159 wheat–Thinopyrum intermedium derivatives, originating from six Zhong partial amphiploids, were evaluated for resistance to powdery mildew (Blumeria graminis f. sp. tritici) at both seedling and adult-plant stages, as well as for field resistance to wheat aphids, together with key agronomic traits. Adult-plant resistance to powdery mildew was common across three years: 34 lines (21.4%) exhibited stable resistance, and 27 (17.0%) were moderately resistant. Resistance frequencies differed among pedigree backgrounds, with Zhong 2 & 5 derivatives showing the highest proportion of stable resistant lines (35.7%). Seedling resistance was detected in 63 lines (39.6%). Aphid resistance was less frequent, with 61 lines (38.4%) classified as resistant, including two highly resistant lines derived from Zhong 3 and Zhong 1 & 3 backgrounds. Combined resistance traits were comparatively rare. Thirty-two lines exhibited resistance to powdery mildew at both seedling and adult-plant stages, while nine lines displayed combined resistance to seedling mildew, adult mildew, and aphids. Analysis of agronomic traits indicated that environmental effects accounted for a substantial proportion of the observed phenotypic variation, whereas pedigree background and resistance responses contributed comparatively little. Correlation analyses revealed generally weak associations between resistance responses and agronomic traits, suggesting that resistance was not a major determinant of agronomic performance within the evaluated population. The identified resistant materials, therefore, represent valuable pre-breeding resources for the incorporation of resistance to multiple biotic stresses in wheat. Further genetic characterization and multi-environment evaluation will facilitate their effective utilization in wheat improvement programs. Full article

Diseases like downy mildew (DM) and powdery mildew (PM) are characterized by whitish symptoms on leaves of many plant species. Hyperspectral imaging (HSI) has been successfully used for the detection and identification of various diseases associated with different symptoms. Proximal HSI (400–1000 nm) was tested under controlled conditions for its potential to differentiate among whitish disease symptoms on leaves of apple and grapevine due to DM, PM, and a non-melanized mutant of apple scab at the leaf and tissue (microscopic) level. Spectral traits were analyzed by using difference spectra and spectral ratios, spectral vegetation indices like NDVI, and average brightness and half NIR increase introduced here and were confirmed by supervised spectral angle mapper classification. Although similar, spectral signatures of whitish symptoms were significantly different and could be used for spectral separation of diseases; differences were greater on the tissue level than on the leaf level. However, disease detection and differentiation were affected by spectral differences between plant species, leaf sides, the variability of symptoms in space and time, and the integrity of superficial pathogen structures. In the case of similar disease symptoms, additional spectral information on the effects of pathogens on plant metabolism, e.g., leaf water patterns, supports spectral differentiation of leaf diseases. Full article

Rubber trees are crucial to the global industrial economy, but they are facing the threat of powdery mildew caused by Erysiphe quercicola. Effective management of this disease depends on early detection. However, traditional monitoring methods are labor-intensive and often inaccurate. This limitation underscores the need for more precise and efficient techniques. This study developed and validated an integrated molecular detection platform that combines quantitative PCR (qPCR), droplet digital PCR (ddPCR), and propidium monoazide (PMA) treatments. The platform demonstrated a robust detection range, accurately quantifying E. quercicola at concentrations as low as 10 spores/mL spore DNA and 10⁻⁵ ng/μL mycelial DNA. Additionally, the system distinguished viable from non-viable spores and detected E. quercicola mycelia in both asymptomatic leaves and aged lesions, significantly enhancing early-stage detection and disease monitoring. This technology also helps assess the efficacy of fungicides against powdery mildew, potentially reducing the use of chemicals and their environmental impact. By improving early diagnosis and disease management, this approach promises to reduce dependence on fungicides and mitigate economic and environmental impacts, highlighting the enormous potential of advanced molecular technologies in sustainable agricultural practices in rubber plantations. Full article

Downy mildew, caused by Plasmopara viticola, is a devastating disease that threatens global grape production, with chemical control remaining the most effective management strategy. However, the repeated application of fungicides has led to widespread resistance in P. viticola populations, while data on the resistance of P. viticola to metalaxyl (MET), cymoxanil (CYM), and cyazofamid (CYA) in China remain limited. In this study, the resistance status of P. viticola to these three fungicides was evaluated across 9 major grape-growing regions in China using leaf-disc bioassays, and potential cross- and multi-resistance patterns were assessed. The majority of isolates (127/233) exhibited either lower resistance (33.48%) or moderate resistance (21.03%) to MET based on the minimum inhibitory concentration (MIC) of 10 μg/mL and 100 μg/mL. Baseline sensitivity profiles for CYM and CYA were established as 8.69 ± 0.64 μg/mL and 0.42 ± 0.05 μg/mL, respectively, using 170 and 137 isolates. The total resistance frequency of P. viticola to CYM was 29.42% (21.18% low resistance, 8.24% moderate resistance), while that to CYA was 28.47% (18.25% low resistance, 9.49% moderate resistance, 0.73% high resistance). A weak but significant positive correlation was detected between CYM and CYA sensitivities (r = 0.193, p = 0.0196), and 13 isolates exhibited resistance to both fungicides, indicating potential multi-resistance risk. Significant regional differences in resistance profiles were observed among populations (p < 0.05), and no overall fitness penalties were detected. These findings highlight the necessity of region-specific and integrated resistance management strategies for sustainable control of grape downy mildew in China. Full article

To implement a successful strawberry (Fragaria × ananassa) farming, fungal diseases must be detected in a timely manner so that informed crop protection decisions can be made. While field scouting is an option, it is manual and labor intensive. Scouting is also inaccurate and reduces efficiency due to micro-climatic lighting and field clutter, among other factors. StrawberryDualNet is a framework that supports Integrated Pest Management and automates symptom surveillance. We present dual-path CNN–Transformer fusion design that integrates two branches: a dynamic multi-scale convolution and a lightweight transformer. The former is able to capture fine-grained morphological lesion textures, while the latter captures overall contextual patterns. The two representations are fused through a learnable gating mechanism to decrease visual uncertainty amongst differing symptoms. We used a stratified five-fold cross-validation to evaluate the framework among five economically significant pathogens. Our approach significantly outperformed other automated scouting baselines, achieving 95.1% accuracy and 95.3% precision, respectively, and it is successful for Anthracnose, Gray Mold, Powdery Mildew, Rhizopus Rot, and Black Spot. The model is also scaled down compared to others (0.04 M parameters; 0.72 MB, 13–20× smaller than MobileNetV2/ShuffleNetV2) and is thus able to be deployed on devices that are lacking computational resources. For edge feasibility, we assessed reduced-precision inference; 16-bit floating point quantization preserved baseline performance at 83 FPS, whereas 8-bit integer quantization caused notable accuracy degradation. Overall, the proposed local–global fusion design provides an accurate, interpretable, and scalable tool for real-time disease phenotyping in precision horticulture. Full article

Downy mildew of hops represents a serious disease affecting hops production in all growing regions. Disease management is primarily based on the application of fungicides at regular intervals based on a short-term forecasting methodology that is essential for evaluating the occurrence of theoretical infections. To enable a more reliable assessment of the pathogen’s presence in a given area, spore traps capturing airborne Pseudoperonospora humuli sporangia can be utilized. The use of quantitative real-time PCR (qRT-PCR) for the detection of sporangia collected by these traps allows for the elimination of laborious and time-consuming microscopic counting. Among four tested P. humuli-specific nuclear DNA sequences, an effective qRT-PCR detection method was developed based on the c127233.5e3 sequence. This detection approach was used for the quantification of sporangia from volumetric spore trap samples collected in situ under field conditions at three selected localities in Bohemia and Moravia during the 2021–2022 period. The obtained results were compared with the short-term forecasting method of the downy mildew (HDM) weather index (I) based on meteorological data. The overall course of the HDM weather index (I) closely correlated with the occurrence of sporangia: after reaching the maximum HDM weather index (I) value, the highest sporangium detection was observed with a time delay of 1–2 weeks at all the monitored sites. The results corresponded well with data obtained from volumetric spore traps in Germany, and the qRT-PCR method proved to be fully comparable to light microscopy. The combination of volumetric spore traps and qRT-PCR can significantly improve the precision of short-term forecasting systems for P. humuli infection, thereby enabling more efficient fungicide application programs in hops protection and contributing to a better understanding of the pathogen’s dispersal dynamics. Full article

Erysiphe corylacearum, the causal agent of powdery mildew in hazelnut (Corylus avellana L.), has become an emerging pathogen of concern in Italian hazelnut production requiring rapid and accurate detection to support timely disease management and phytosanitary measures. We developed and validated a field-deployable loop-mediated isothermal amplification (LAMP) assay for the specific detection of E. corylacearum and evaluated three primer sets targeting the Internal Transcribed Spacer (ITS) region, RNA polymerase II second largest subunit (rpb2), and glutamine synthetase (GS) genes; the GS-targeting Ecg set showed the highest sensitivity and specificity. The assay was shown to be sensitive down to 200 fg of fungal DNA, efficiently detected E. corylacearum from diluted crude leaf extracts, and produced results within half an hour, allowing the detection of latent infections before visible symptoms emerged. On-site validation with a portable LAMP instrument showed the assay’s suitability for field-deployable diagnosis and early-warning applications in hazelnut orchards. Full article

Cucumber downy mildew, caused by the obligate parasitic oomycete Pseudoperonospora cubensis [(Berkeley & M. A. Curtis) Rostovzev], is a major threat to global cucumber production. Effective disease management relies on rapid and accurate pathogen detection. However, due to the specialized parasitic nature of P. cubensis, conventional methods are often laborious, low-throughput and inadequate, necessitating the development of a new approach for high-throughput sporangia counting. To address this limitation, we developed a rapid, high-throughput flow cytometry (FCM) assay for the direct quantification of P. cubensis sporangia. The optimal staining protocol involved adding 30 µL of 1000× diluted SYBR Green I to 500 µL of sporangial suspension and incubating at room temperature for 20 min. The flow cytometry parameters were set to a high sample loading speed with a 30-s acquisition time. Instrumental settings included an FL1 (green fluorescence) threshold of 8 × 10⁴ and an SSC (side scatter) threshold of 3 × 10⁵, with low gain. Validation against hemocytometer counts revealed a strong positive correlation (r = 0.8352). The assay demonstrated high reproducibility, with relative standard deviations (RSDs) ranging from 1.96–9.84%, and a detection limit of 1–10 sporangia/µL. Operator-dependent variability ranged from 8.85% to 18.79%. These results confirm that the established flow cytometry assay is a reliable and efficient tool for P. cubensis quantification, offering considerable potential for improving cucumber downy mildew monitoring and control strategies. Full article

Cucumber downy mildew, angular leaf spot, and powdery mildew represent three predominant fungal diseases that substantially compromise cucumber yield and quality. To address the challenges posed by the irregular morphology, prominent multi-scale characteristics, and ambiguous lesion boundaries of cucumber foliar diseases in complex field environments—which often lead to insufficient detection accuracy—along with the existing models’ difficulty in balancing high precision with lightweight deployment, this study presents YOLOv11n-DSU (a lightweight hierarchical detection model engineered using the YOLOv11n architecture). The proposed model integrates three key enhancements: deformable convolution (DEConv) for optimized feature extraction from irregular lesions, a spatial and channel-wise attention (SCSA) mechanism for adaptive feature refinement, and a Unified Intersection over Union (Unified-IoU) loss function to improve localization accuracy. Experimental evaluations demonstrate substantial performance gains, with mean Average Precision at 50% IoU threshold (mAP₅₀) and mAP_50–95 increasing by 7.9 and 10.9 percentage points, respectively, and precision and recall improving by 6.1 and 10.0 percentage points. Moreover, the computational complexity is markedly reduced to 5.8 Giga Floating Point Operations (GFLOPs). Successful deployment on an embedded platform confirms the model’s practical viability, exhibiting robust real-time inference capabilities and portability. This work provides an accurate and efficient solution for automated disease grading in field conditions, enabling real-time and precise severity classification, and offers significant potential for advancing precision plant protection and smart agricultural systems. Full article

This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study’s results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production. Full article

Hexaconazole, a triazole-class fungicide, demonstrates broad-spectrum protective and therapeutic activity against fungal pathogens, particularly those from Basidiomycota and Ascomycota, such as brown spot and powdery mildew. Despite its efficacy in controlling Actinidia brown spot disease in kiwifruit, excessive hexaconazole residues pose significant health risks due to its high toxicity. To address this challenge, a rapid analytical method for detecting hexaconazole residues in kiwifruit was developed using surface-enhanced Raman spectroscopy (SERS). The methodology employed silver colloid (C-AgNPs) as the active substrate and 1 mol/L NaCl as the aggregation agent, optimized through systematic testing, resulting in an optimal volume ratio of 400:225 between C-AgNPs and hexaconazole solution and a sequential mixing order of C-AgNPs + NaCl + Hexaconazole, followed by a 20 min incubation period. The characteristic Raman peak at 1584 cm⁻¹ was identified as the spectral signature for hexaconazole quantification. Analytical validation revealed a linear detection range of 0.25–2.25 mg/L (R² = 0.9870), precision with a relative standard deviation (RSD) of 1.7%, and an average recovery rate of 88.40–105.50%, confirming the method’s robustness. This approach enables rapid, non-destructive analysis with minimal sample pretreatment, offering high sensitivity and stability. This method demonstrates great potential for detecting hexaconazole residues in agricultural products. Full article

Downy mildew, caused by Pseudoperonospora cubensis, remains a major constraint to cucumber (Cucumis sativus L.) production worldwide. The erosion of resistance conferred by the historic dm-1 gene has heightened the quest for new and enduring sources of resistance. PI 197085, a resistant accession identified under Central European field conditions, remains largely genetically unexplored. In this study, an evenly saturated genetic linkage map was developed using an F₂ population derived from PI 197085 × PI 175695, which comprised 164 polymorphic markers spanning all seven chromosomes. Composite interval mapping revealed five quantitative trait loci (QTLs) linked to resistance against P. cubensis, distributed across chromosomes 2, 3, 4, and 5. Candidate gene analysis within the QTL intervals identified clusters of receptor-like kinases, transcription factors, and redox-related enzymes, suggesting that resistance in PI 197085 is polygenic and regulator-rich. The improved resolution of the linkage map enabled more precise localization of resistance loci and uncovered novel genomic regions that were not previously detected in this population. These findings provide a foundation for marker-assisted selection and fine-mapping efforts aimed at developing cucumber cultivars with the robust and durable resistance to P. cubensis. Full article

Deep learning-based plant disease classification models often suffer from performance degradation when training data are limited. Hence, generative models offer a promising solution for model performance in plant disease classification. In this work, images representing powdery mildew, downy mildew, and healthy plant leaves were generated using traditional augmentation methods as well as both DCGAN and a modified DCGAN featuring residual connection blocks with varied activation functions. Evaluation metrics IS and FID revealed that the modified DCGAN consistently produced generative images with strong class-distinctive features and greater overall diversity compared to basic GAN methods, with an IS increment of 7.9% to 11.54% and FID decrement of 6.6% to 7.8%. After selecting the best augmentation method, we input the generated images into the training sets for the classification models, AlexNet, VGG16, and Goog-LeNet, to measure improvements in disease recognition. All classifiers benefited from the augmented datasets, with the modified DCGAN-based augmentation yielding the highest precision, recall, and accuracy. GoogLeNet outperformed all classification models, with an overall precision, recall, and F1-Score value of 98%. Notably, this generative approach minimized errors between visually similar categories, such as powdery mildew and healthy samples, by capturing subtle morphological differences. The results confirm that class-aware generative augmentation can both expand the number of training images and preserve the critical features necessary for discrimination, significantly boosting model effectiveness. These advances show the practical potential of generative models not only to enrich datasets but also to improve the accuracy and robustness of plant disease detection for real-world agricultural scenarios. Full article

Wheat powdery mildew poses a serious threat to crop growth and yield, highlighting the critical need for accurate detection to ensure food security and maintain agricultural productivity. This study explores the integration of polarization remote sensing with a Multi-Verse Optimizer (MVO)–enhanced Random Forest (RF) model for disease detection. Polarization imaging equipment was used to extract key polarization parameters, including the degree of polarization (DOP) and angle of polarization (AOP), from wheat leaves to capture subtle structural differences between healthy and diseased tissues. The MVO algorithm was employed to optimize RF hyperparameters, thereby improving classification performance and addressing the limitations of manual parameter tuning and conventional machine learning methods. Several machine learning algorithms were also evaluated for comparison. The results indicate that the proposed MVO_RF approach outperformed traditional methods, achieving an F1-score of 0.9715, a Kappa coefficient of 0.9797, and an overall accuracy of 0.9878. These findings demonstrate that the integration of polarization characteristics with MVO-optimized machine learning establishes a robust and efficient framework for monitoring wheat powdery mildew. More importantly, it facilitates early in-field disease warnings, enhances the accuracy and efficiency of targeted pesticide application, and offers quantitative decision-making support for smart agricultural management and disease prevention strategies. 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