Search Results (18)

Apples (Malus domestica), one of Iran’s oldest cultivated fruit crops, hold considerable economic importance. In this study, 170 apple leaf samples representing various commercial cultivars were collected across the country. RT-PCR screening targeted five common apple-infecting viruses and two viroids: apple chlorotic leaf spot virus (ACLSV), apple stem pitting virus (ASPV), apple stem grooving virus (ASGV), apple green crinkle-associated virus (AGCaV), apple mosaic virus (ApMV), apple scar skin viroid (ASSVd), and hop stunt viroid (HSVd). To identify additional or novel agents, 40 RT-PCR-negative samples were pooled into two composite groups and analyzed using next-generation sequencing (NGS). NGS was also performed on individual samples with mixed infections to retrieve full genomes. RT-PCR confirmed the presence of ACLSV, ASPV, ASGV, AGCaV, ApMV, and HSVd. NGS further revealed three additional pathogens: citrus concave gum-associated virus (CCGaV), apple hammerhead viroid (AHVd), and apricot vein clearing-associated virus (AVCaV), which were subsequently detected across the collection by RT-PCR. AGCaV was most prevalent (47.6%), followed by ACLSV (45.8%), HSVd (27.6%), AVCaV (20.5%), ASGV (17%), AHVd (15.2%), ASPV (14.1%), CCGaV (4.7%), and ApMV (3.5%). Mixed infections occurred in 67% of samples. Phylogenetic analysis based on CP genes (ACLSV, ASGV, AGCaV) and full genomes (AVCaV, AHVd) clustered Iranian isolates together, suggesting a common origin. This is the first report in Iran of AGCaV, CCGaV, ApMV, and AVCaV in apple, and notably, the first global report of AVCaV in a non-Prunus host. The findings provide the first comprehensive assessment of the sanitary status of apple trees in Iran. Full article

Apple mosaic disease (AMD) is a widespread viral disease affecting apple-growing regions around the world. Recent studies have identified a novel ilarvirus, apple necrotic mosaic virus (ApNMV), as the major causal agent of AMD in China. However, the molecular mechanisms underlying AMD pathogenesis and the global gene expression changes during mosaic symptom development remain largely unknown. In this study, we performed transcriptome analysis to investigate apple gene responses to AMD. A total of 815 differentially expressed genes (DEGs) were identified in mosaic leaves compared to healthy controls, while 1050 DEGs were found between symptomless leaves (infected with ApNMV) and mosaic leaves. Functional enrichment analysis revealed that these DEGs were predominantly involved in carbohydrate metabolism, oxidation-reduction processes, secondary metabolite biosynthesis, and plant hormone signal transduction. Further biological assays demonstrated that the manifestation of mosaic symptoms in apple leaves was associated with reactive oxygen species (ROS) accumulation and downregulation of ROS-scavenging genes. Collectively, our findings provide new insights into the molecular basis of ApNMV-induced mosaic symptom development in apple and offer potential targets for the management of AMD. Full article

Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards. Full article

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R² = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R² = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R² = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy. Full article

Mosaic of apple leaves is a major disease that reduces the yield and quality of apples, and monitoring for the disease allows for its timely control. However, few studies have investigated the status of apple pests and diseases, especially mosaic diseases, using hyperspectral imaging technology. Here, hyperspectral images of healthy and infected apple leaves were obtained using a near-ground imaging high spectrometer and the anthocyanin content was measured simultaneously. The spectral differences between the healthy and infected leaves were analyzed. The content of anthocyanin in the leaves was estimated by the optimal model to determine the degree of apple mosaic disease. The leaves exhibited stronger reflectance at a range of 500–560 nm as the degree of disease increased. The correlation between the spectral reflectance processed by the Gaussian1 wavelet transform and anthocyanin was significantly improved compared to the corresponding correlation results with the original spectrum. The VPs-XGBoost anthocyanin estimation model performed the best, which was sufficient to monitor the degree of the disease. The findings provide theoretical support for the quantitative estimation of leaf anthocyanin content by remote sensing to monitor the degree of disease; they lay the foundation for large-scale monitoring of the degree of apple mosaic disease by remote sensing. Full article

The infection of Apple mosaic virus (ApMV) can severely damage the cellular structure of apple leaves, leading to a decrease in leaf chlorophyll content (LCC) and reduced fruit yield. In this study, we propose a novel method that utilizes hyperspectral imaging (HSI) technology to non-destructively monitor ApMV-infected apple leaves and predict LCC as a quantitative indicator of disease severity. LCC data were collected from 360 ApMV-infected leaves, and optimal wavelengths were selected using competitive adaptive reweighted sampling algorithms. A high-precision LCC inversion model was constructed based on Boosting and Stacking strategies, with a validation set $R_v^2$ of 0.9644, outperforming traditional ensemble learning models. The model was used to invert the LCC distribution image and calculate the average and coefficient of variation (CV) of LCC for each leaf. Our findings indicate that the average and CV of LCC were highly correlated with disease severity, and their combination with sensitive wavelengths enabled the accurate identification of disease severity (validation set overall accuracy = 98.89%). Our approach considers the role of plant chemical composition and provides a comprehensive evaluation of disease severity at the leaf scale. Overall, our study presents an effective way to monitor and evaluate the health status of apple leaves, offering a quantifiable index of disease severity that can aid in disease prevention and control. Full article

Anthocyanins are severity indicators for apple mosaic disease and can be used to monitor tree health. However, most of the current studies have focused on healthy leaves, and few studies have estimated the anthocyanin content in diseased leaves. In this study, we obtained the hyperspectral data of apple leaves with mosaic disease, analyzed the spectral characteristics of leaves with different degrees of Mosaic disease, constructed and screened the spectral index sensitive to anthocyanin content, and improved the estimation model. To improve the conciseness of the model, we integrated Variable Importance in Projection (VIP), Partial Least Squares Regression (PLSR), and Akaike Information Criterion (AIC) to select the optimal PLSR model and its independent variables. Sparrow Search Algorithm-Random Forest (SSA-RF) was used to improve accuracy. Results showed the following: (1) anthocyanin content increased gradually with the aggravation of disease. The reflectance of the blade spectrum in the visible band increased, the red edge moved to short wave, and the phenomenon of “blue shift of spectrum” occurred. (2) The VIP-PLSR-AIC selected 17 independent variables from 21 spectral indices. (3) Variables were used to construct PLSR, Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF), and SSA-RF to estimate anthocyanin content. Results showed the estimation accuracy and stability of the SSA-RF model were better than other models. The model set determination coefficient ($R^2$) was up to 0.955, which is 0.047 higher than that of the RF model and 0.138 higher than that of the SVM model with the lowest accuracy. The model was constructed at the leaf scale and can provide a reference for other scale studies, including a theoretical basis for large-area, high-efficiency, high-precision anthocyanin estimation and monitoring of apple mosaics using remote sensing technology. Full article

Besides apple mosaic virus (ApMV), apple necrotic mosaic virus (ApNMV) has also been found to be associated with apple mosaic disease. Both viruses are unevenly distributed throughout the plant and their titer decreases variably with high temperatures, hence requiring proper tissue and time for early and real-time detection within plants. The present study was carried out to understand the distribution and titer of ApMV and ApNMV in apple trees from different plant parts (spatial) during different seasons (temporal) for the optimization of tissue and time for their timely detection. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) was carried out to detect and quantify both viruses in the various plant parts of apple trees during different seasons. Depending on the availability of tissue, both ApMV and ApNMV were detected in all the plant parts during the spring season using RT-PCR. During the summer, both viruses were detected only in seeds and fruits, whereas they were detected in leaves and pedicel during the autumn season. The RT-qPCR results showed that during the spring, the ApMV and ApNMV expression was higher in leaves, whereas in the summer and autumn, the titer was mostly detected in seeds and leaves, respectively. The leaves in the spring and autumn seasons and the seeds in the summer season can be used as detection tissues through RT-PCR for early and rapid detection of ApMV and ApNMV. This study was validated on 7 cultivars of apples infected with both viruses. This will help to accurately sample and index the planting material well ahead of time, which will aid in the production of virus-free, quality planting material. Full article

The mosaic leafhopper, Orientus ishidae (Matsumura), is an Asian species widespread in Europe that can cause leaf damage in wild trees and transmit disease phytoplasmas to grapevines. Following an O. ishidae outbreak reported in 2019 in an apple orchard in northern Italy, the biology and damage caused by this species to apples were investigated during 2020 and 2021. Our studies included observations on the O. ishidae life cycle, leaf symptoms associated to its trophic activity, and its capability to acquire “Candidatus Phytoplasma mali,” a causal agent of Apple Proliferation (AP). The results indicate that O. ishidae can complete the life cycle on apple trees. Nymphs emerged between May and June, and adults were present from early July to late October, with the peak of flight between July and early August. Semi-field observations allowed for an accurate description of leaf symptoms that appeared as a distinct yellowing after a one-day exposure. In field experiments, 23% of the leaves were found damaged. In addition, 16–18% of the collected leafhoppers were found carrying AP phytoplasma. We conclude that O. ishidae has the potential to be a new apple tree pest. However, further studies are required to better understand the economic impact of the infestations. Full article

Melatonin is a multifunctional and ubiquitous molecule. In animals, melatonin is a hormone that is involved in a wide range of physiological activities and is also an excellent antioxidant. In plants, it has been considered a master regulator of multiple physiological processes as well as of hormonal homeostasis. Likewise, it is known for its role as a protective biomolecule and activator of tolerance and resistance against biotic and abiotic stress in plants. Since infections by pathogens such as bacteria, fungi and viruses in crops result in large economic losses, interest has been aroused in determining whether melatonin plays a relevant role in plant defense systems against pathogens in general, and against viruses in particular. Currently, several strategies have been applied to combat infection by pathogens, one of them is the use of eco-friendly chemical compounds that induce systemic resistance. Few studies have addressed the use of melatonin as a biocontrol agent for plant diseases caused by viruses. Exogenous melatonin treatments have been used to reduce the incidence of several virus diseases, reducing symptoms, virus titer, and even eradicating the proliferation of viruses such as Tobacco Mosaic Virus, Apple Stem Grooving Virus, Rice Stripe Virus and Alfalfa Mosaic Virus in tomato, apple, rice and eggplant, respectively. The possibilities of using melatonin as a possible natural virus biocontrol agent are discussed. Full article

Rose (Rosa spp.), especially R. hybrida, is one of the most popular ornamental plants in the world and the third largest cut flower crop in Taiwan. Rose mosaic disease (RMD), showing mosaic, line patterns and ringspots on leaves, is a common rose disease caused by the complex infection of various viruses. Due to pests and diseases, the rose planting area in Taiwan has been decreasing since 2008; however, no rose virus disease has been reported in the past five decades. In the spring of 2020, rose samples showing RMD-like symptoms were observed at an organic farm in Chiayi, central Taiwan. The virome in the farm was analyzed by RNA-seq. Rose genomic sequences were filtered from the obtained reads. The remaining reads were de novo assembled to generate 294 contigs, 50 of which were annotated as viral sequences corresponding to 10 viruses. Through reverse transcription-polymerase chain reaction validation, a total of seven viruses were detected, including six known rose viruses, namely apple mosaic virus, prunus necrotic ringspot virus, rose partitivirus, apple stem grooving virus, rose spring dwarf-associated virus and rose cryptic virus 1, and a novel ilarvirus. After completing the whole genome sequencing and sequence analysis, the unknown ilarvirus was demonstrated as a putative new species, tentatively named rose ilarvirus 2. This is the first report of the rose virus disease in Taiwan. Full article

Viral diseases affecting the essential oil rose, which is a valuable object of agricultural production, may have a significant negative impact on the economic value of this crop. Hence, the study and control of potentially dangerous viruses is essential to improving the quality of cultivars of this raw plant material, to enable production of valuable derivatives. The diversity of viruses affecting Rosa L. plants manifests itself in their conditional division into those that are specific to this crop, and those that are hosted by other plants. Representatives of both groups are found in different countries, however, a low number of viruses identified have been thoroughly studied through the use of experimental methods. In particular, with regard to many viruses, the issue of their spread remains open. The viruses infecting Rosa L. plants along with other crops are described in the literature in detail, as the range of hosts they affect is rather wide and well-studied. It is also possible to single out the three most significant viruses affecting this host—Prunus necrotic ringspot virus, Apple mosaic virus and Arabis mosaic virus which individually, or collectively, cause viral diseases that manifest themselves in mosaic symptoms. The most likely mechanisms for the spread of the Rosa L. species viruses are vegetative propagation procedures and transmission by various pests. These presumptions underlie viral infection control methods, including a well-thought-out planting scheme and provision of accurate plant care, which considers plant disinfection, disease monitoring associated with diagnostics and obtaining virus-free material through biotechnology techniques. Full article

The diagnosis of virus infection can facilitate the effective control of plant viral diseases. To date, serological and molecular methods for the detection of virus infection have been widely used, but these methods have disadvantages if applied for broad-range and large-scale detection. Here, we investigated the effect of infection of several different plant RNA and DNA viruses such as cucumber mosaic virus (CMV), tobacco mosaic virus (TMV), potato virus X (PVX), potato virus Y (PVY) and apple geminivirus on starch content in leaves of Nicotiana benthamiana. Analysis showed that virus infection at an early stage was generally associated with a reduction in starch accumulation. Notably, a reduction in starch accumulation was readily apparent even with a very low virus accumulation detected by RT-PCR. Furthermore, we also observed that the infection of three latent viruses in propagative apple materials was associated with a reduction in starch accumulation levels. Analysis of transcriptional expression showed that some genes encoding enzymes involved in starch biosynthesis were downregulated at the early stage of CMV, TMV, PVX and PVY infections, suggesting that virus infection interferes with starch biosynthesis in plants. Our findings suggest that assessing starch accumulation levels potentially serve as a broad-range indicator for the presence of virus infection. Full article

Many viruses have been found associated with apple mosaic disease in different parts of the world. In order to reveal and characterize the viruses and viroids in symptomatic apple plants, next-generation sequencing (RNA seq.) of rRNA-depleted total RNA using Illumina Hiseq2500 was applied to two cultivars, Oregon Spur and Golden Delicious, with symptoms of mosaic and necrosis and one cultivar, Red Fuji, which was asymptomatic. The RNA sequencing detected five viruses, viz., apple necrotic mosaic virus (ApNMV), apple mosaic virus (ApMV), apple stem grooving virus (ASGV) and apple stem pitting virus (ASPV), apple chlorotic leaf spot virus (ACLSV), and one viroid i.e., apple hammerhead viroid (AHVd). RT-PCR amplification and sequencing also confirmed the presence of all these five viruses and viroids detected in HTS of total RNA. The complete genomes of five viruses and AHVd were reconstructed. The phylogenetic analysis of these viruses and AHVd revealed genetic diversity by forming subclusters with isolates from other countries. Recombination events were observed in all five viruses while single-nucleotide variants were detected only in ApMV and ApNMV. The absence of ApMV and ApNMV in asymptomatic samples from the same cultivars in an RT-PCR assay indicated that these two viruses are associated with mosaic disease of apples in India. This is the first viral genome analysis of symptomatic and asymptomatic apple plants and the first report of genome characterization of viruses associated with apple mosaic disease from India. High-throughput RNA sequencing is a powerful tool to characterize the genome of viruses and viroids in plants previously undetected by conventional methods. This would also help in the indexing and certification of large-scale germplasm. Full article

Apple mosaic disease is one of the most widely distributed and destructive diseases in apple cultivation worldwide, especially in China, whose apple yields account for more than 50% of the global total. Apple necrotic mosaic virus (ApNMV) is a newly identified ilarvirus that is closely associated with apple mosaic disease in China; however, basic viral protein interactions that play key roles in virus replication and the viral life cycle have not been determined in ApNMV. Here, we first identify an ApNMV–Lw isolate that belongs to subgroup 3 in the genus Ilarvirus. ApNMV–Lw was used to investigate interactions among viral components. ApNMV 1a and 2a^pol, encoded by RNA1 and RNA2, respectively, were co-localized in plant cell cytoplasm. ApNMV 1a interacted with itself at both the inter- and intramolecular levels, and its N-terminal portion played a key role in these interactions. 1a also interacted with 2a^pol, and 1a’s C-terminal, together with 2a^pol’s N-terminal, was required for this interaction. Moreover, the first 115 amino acids of 2a^pol were sufficient for permitting the 1a–2a^pol interaction. This study provides insight into the protein interactions among viral replication components of ApNMV, facilitating future investigations on its pathogenicity, as well as the development of strategies to control the virus and disease. Full article