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Cotton verticillium wilt significantly impacts both cotton quality and yield. Selecting disease-resistant varieties and using their resistance genes in breeding is an effective and economical control measure. Accurate severity estimation of this disease is crucial for breeding resistant cotton varieties. However, current methods fall short, slowing the breeding process. To address these challenges, this paper introduces CVW-Etr, a high-precision method for estimating the severity of cotton verticillium wilt. CVW-Etr classifies severity into six levels (L0 to L5) based on the proportion of segmented diseased leaves to lesions. Upon integrating YOLOv8-Seg with MobileSAM, CVW-Etr demonstrates excellent performance and efficiency with limited samples in complex field conditions. It incorporates the RFCBAMConv, C2f-RFCBAMConv, AWDownSample-Lite, and GSegment modules to handle blurry transitions between healthy and diseased regions and variations in angle and distance during image collection, and to optimize the model's parameter size and computational complexity. Our experimental results show that CVW-Etr effectively segments diseased leaves and lesions, achieving a mean average precision (mAP) of 92.90% and an average severity estimation accuracy of 92.92% with only 2.6M parameters and 10.1G FLOPS. Through experiments, CVW-Etr proves robust in estimating cotton verticillium wilt severity, offering valuable insights for disease-resistant cotton breeding applications. [ABSTRACT FROM AUTHOR]

Verticillium wilt (VW) is a disruptive symptom caused by Verticillium dahliae, which affects fiber yield and quality of cotton in China. AP2/ERF transcription factors play prominent role in plant growth, development and stress response. In order to excavate the genes related to resistance to VW in cotton and explore their biological functions, the candidate gene GhERF020 (XM_016842745.1) was obtained by homologous alignment of AT1G22810.1 related gene of Arabidopsis thaliana against V. dahliae. Bioinformatic analysis showed that the open reading frame length of GhERF020 was 441 bp, encoded 147 amino acids. GhERF020 contained a conserved AP2 domain, the relative molecular weight was 16.16 kD without transmembrane structure, belonging to the hydrophilic protein. It was predicted that the promoter region contained response elements of salicylic acid (SA), methyl jasmonate (MEJA), abscisic acid (ABA) and other response elements. The expression of GhERF020 was induced by V. dahliae infection. Subcellular localization analysis showed that GhERF020 protein was localized in the nucleus. As the expression of GhERF020 was downregulated by virus-induced gene silencing (VIGS) technique, the susceptibility of cotton plants to V. dahliae was significantly enhanced, and the disease index and pathogen biomass were distinctly increased. Above results indicated that GhERF020 was a positive regulator in resistance against V. dahliae in cotton. [ABSTRACT FROM AUTHOR]

Abstract Background In agricultural production, fungal diseases significantly impact the yield and quality of cotton (Gossypium spp.) with Verticillium wilt posing a particularly severe threat. Results This study is focused on investigating the effectiveness of endophytic microbial communities present in the seeds of disease-resistant cotton genotypes in the control of cotton Verticillium wilt. The technique of 16S ribosomal RNA (16S rRNA) amplicon sequencing identified a significant enrichment of the Bacillus genus in the resistant genotype Xinluzao 78, which differed from the endophytic bacterial community structure in the susceptible genotype Xinluzao 63. Specific enriched strains were isolated and screened from the seeds of Xinluzao 78 to further explore the biological functions of seed endophytes. A synthetic microbial community (SynCom) was constructed using the broken-rod model, and seeds of the susceptible genotype Xinluzao 63 in this community that had been soaked with the SynCom were found to significantly control the occurrence of Verticillium wilt and regulate the growth of cotton plants. Antibiotic screening techniques were used to preliminarily identify the colonization of strains in the community. These techniques revealed that the strains can colonize plant tissues and occupy ecological niches in cotton tissues through a priority effect, which prevents infection by pathogens. Conclusion This study highlights the key role of seed endophytes in driving plant disease defense and provides a theoretical basis for the future application of SynComs in agriculture.

Background: In agricultural production, fungal diseases significantly impact the yield and quality of cotton (Gossypium spp.) with Verticillium wilt posing a particularly severe threat. Results: This study is focused on investigating the effectiveness of endophytic microbial communities present in the seeds of disease-resistant cotton genotypes in the control of cotton Verticillium wilt. The technique of 16S ribosomal RNA (16S rRNA) amplicon sequencing identified a significant enrichment of the Bacillus genus in the resistant genotype Xinluzao 78, which differed from the endophytic bacterial community structure in the susceptible genotype Xinluzao 63. Specific enriched strains were isolated and screened from the seeds of Xinluzao 78 to further explore the biological functions of seed endophytes. A synthetic microbial community (SynCom) was constructed using the broken-rod model, and seeds of the susceptible genotype Xinluzao 63 in this community that had been soaked with the SynCom were found to significantly control the occurrence of Verticillium wilt and regulate the growth of cotton plants. Antibiotic screening techniques were used to preliminarily identify the colonization of strains in the community. These techniques revealed that the strains can colonize plant tissues and occupy ecological niches in cotton tissues through a priority effect, which prevents infection by pathogens. Conclusion: This study highlights the key role of seed endophytes in driving plant disease defense and provides a theoretical basis for the future application of SynComs in agriculture. [ABSTRACT FROM AUTHOR]

Pseudomonas nitroreducens L4 was isolated from the interior of cotton plants, which showed strong biocontrol activity against Verticillium dahlia and other fungal pathogens. To elucidate the biocontrol mechanism, the genome sequence of L4 was sequenced using the Illumina and Nanopore sequencing platform. The assembled genome of L4 consisted of a single circular chromosome was 6,229,472 bp, with an average GC content of 64.95 %, 5,629 protein-coding genes, 72 tRNA, 16 rRNA and 1 tm RNA. Six secondary metabolite biosynthetic gene clusters are identified in the genome. The genome sequence provided a theoretical basis for analyzing the biocontrol mechanism of this strain.

Abstract Background Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively. Result The results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%. Conclusions The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.

Verticillium wilt (VW) is a common soilborne disease of cotton. It occurs mainly in the seedling and boll-opening stages and severely impairs the yield and quality of the fiber. Rapid and accurate identification and evaluation of VW severity (VWS) forms the basis of field cotton VW control, which has great significance to cotton production. Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses, which require abundant time and professional expertise. Remote and proximal sensing using imagery and spectrometry have great potential for this purpose. In this study, we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values, in situ images, and spectra of 361 cotton canopies. To estimate cotton VWS values at the canopy scale, we developed two deep learning approaches that use in situ images and spectra, respectively. For the imagery-based method, given the high complexity of the in situ environment, we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes (CFS) dataset with over 1000 images for each scene-unit type. We performed pretrained convolutional neural networks (CNNs) training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy. The results showed that the DarkNet-19 model achieved satisfactory performance in CFS classification and VWS values estimation (R2 = 0.91, root-mean-square error (RMSE) = 6.35%). For the spectroscopy-based method, we first designed a one-dimensional regression network (1D CNN) with four convolutional layers. After dimensionality reduction by sensitive-band selection and principal component analysis, we fitted the 1D CNN with varying numbers of principal components (PCs). The 1D CNN model with the top 20 PCs performed best (R2 = 0.93, RMSE = 5.77%). These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

Verticillium wilt is one of the most crucial diseases caused by Verticillium dahliae that threatens the cotton industry. Statistical results showed that the return of cotton plants infected with V. dahliae to the field might be an essential cause of the continuous aggravation of cotton Verticilliumwilt. The correlation among the cotton plants infected with V. dahliae returning to the field, the occurrence of Verticillium wilt, and the number of microsclerotia in rhizosphere soil need further investigation. A potted experiment was carried out to explore the effects of the direct return of cotton plants infected with Verticillium dahliae to the field on the subsequent growth and Verticillium wilt occurrence in cotton. As a risk response plan, we investigated the feasibility of returning dung-sand (i.e., insect excreta) to the field, the dung-sand was from the larvae of Protaetia brevitarsis (Coleoptera: Cetoniidea) that were fed with the V. dahliae--infected cotton plants. The results demonstrated that the return of the entire cotton plants to the field presented a promotional effect on the growth and development of cotton, whereas the return of a single root stubble or cotton stalks had an inhibitive effect. The return of cotton stalks and root stubble infected with V. dahliae increased the risk and degree of Verticillium wilt occurrence. The disease index of Verticillium wilt occurrence in cotton was positively correlated with the number ofmicrosclerotia in the rhizosphere soil. The disease index increased by 20.00%, and the number of soil microsclerotia increased by 8.37 fold in the treatment of returning root stubble infected with V. dahliae to the field. No Verticillium wilt microsclerotia were detected in the feed prepared from cotton stalks and root stubble fermented formore than 5 days or in the transformed dungsand. There was no risk of inoculation with Verticillium wilt microsclerotia when the dung-sand was returned to the field. The indirect return of cotton plants infected with V. dahliae to the field by microorganism--insect systems is worthy of further exploration plan of the green prevention and control for Verticillium wilt and the sustainable development of the cotton industry. [ABSTRACT FROM AUTHOR]

Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the original spectra were processed using Savitzky–Golay (SG) smoothing, and on its basis through mean centering, standard normal variate (SG-SNV), multiplicative scatter correction (SG-MSC), reciprocal second-order differentiation, and logarithmic second-order differentiation ([lg(SG)]″) preprocessing operations. The characteristic bands were selected based on the correlation coefficient, vegetation index, successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS). The single-factor model, back propagation neural network of particle swarm optimization algorithm, and extreme learning machine (ELM) of a grey wolf optimizer (GWO) algorithm were constructed to compare and explore the ability of each model to estimate the soil plant analysis development (SPAD) value of cotton under VW stress. The results showed that spectral pretreatment could improve the correlation between characteristic bands and SPAD values. SG-MSC and SG-SNV showed better changes in the five pretreatments, and the maximum correlation coefficients of healthy and VW cotton leaves were higher than 0.74. Compared with SPA, the accuracy of model estimation based on CARS-extracted characteristic bands was higher, and the estimation accuracy of the multi-factor model was better than that of the single-factor model under each pretreatment. For healthy cotton leaves, [lg(SG)]″–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.956 and 0.887, respectively. For VW cotton leaves, SG-MSC–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.832 and 0.824, respectively. Therefore, the GWO–ELM model constructed under different pretreatments combined with characteristic extraction methods can be used for the estimation of leaf SPAD values under VW stress to dynamically monitor VW stress in cotton and provide a theoretical reference for precision agriculture. [ABSTRACT FROM AUTHOR]

Cotton Verticillium wilt is a common fungal disease during the growth of cotton, leading to the yellowing of leaves, stem dryness, and root rot, severely affecting the yield and quality of cotton. Current monitoring methods for Verticillium wilt mainly rely on manual inspection and field investigation, which are inefficient and costly, and the methods of applying pesticides in cotton fields are singular, with issues of low pesticide efficiency and uneven application. This study aims to combine UAV remote sensing monitoring of cotton Verticillium wilt with the precision spraying characteristics of agricultural drones, to provide a methodological reference for monitoring and precision application of pesticides for cotton diseases. Taking the cotton fields of Shihezi City, Xinjiang as the research subject, high-resolution multispectral images were collected using drones. Simultaneously, 150 sets of field samples with varying degrees of Verticillium wilt were collected through ground data collection, utilizing data analysis methods such as partial least squares regression (PLSR) and neural network models; additionally, a cotton Verticillium wilt monitoring model based on drone remote sensing images was constructed. The results showed that the estimation accuracy R2 of the PLSR and BP neural network models based on EVI, RENDVI, SAVI, MSAVI, and RDVI vegetation indices were 0.778 and 0.817, respectively, with RMSE of 0.126 and 0.117, respectively. Based on this, an analysis of the condition of the areas to be treated was performed, combining the operational parameters of agricultural drones, resulting in a prescription map for spraying against cotton Verticillium wilt.

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively. Result: The results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%. Conclusions: The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt. [ABSTRACT FROM AUTHOR]

In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease. [ABSTRACT FROM AUTHOR]

Abstract Background Cotton Verticillium wilt, causing by Verticillium dahliae, has seriously affected the yield and quality of cotton. The incidence of Verticillium wilt in cotton fields has been on the rise for many years, especially after straw has been returned to the fields. Intercropping can reduce the incidence of soil borne diseases and is often used to control crop diseases, but the relationship between the effects of intercropping on microbial communities and the occurrence of plant diseases is unclear. This research explored the relationship between soil microbial community structure and Cotton Verticillium wilt in interplanting of cotton-onion, cotton-garlic, cotton-wheat and cotton monocultures. Amplicon sequencing applied to the profile of bacterial and fungal communities. Results The results showed that the disease index of Cotton Verticillium wilt was significantly reduced after intercropping with cotton-garlic and cotton-onion. Chao1 and Sobs indices were not significantly different in the rhizosphere soil and pre-plant soils of the four planting patterns, but the pre-plant fungal shannon index was significantly lower in the cotton-onion intercropping plot than in the other three plots. PCoA analysis showed that the soil microbial communities changed to a certain extent after intercropping, with large differences in the microbial communities under different cropping patterns. The abundance of Chaetomium was highest in the cotton-garlic intercropping before planting; the abundance of Penicillium was significantly higher in the cotton-wheat intercropping than in the other three systems. Conclusion Cotton-garlic and cotton-onion interplanting can control Cotton Verticillium wilt by affecting the soil microbial community. Fungi of the genera Chaetomium and Penicillium may be associated with plant disease resistance.

Abstract Background In our previous study, a strain EBS03 with good biocontrol potential was screened out of 48 strains of cotton endophyte Bacillus subtilis by evaluating the controlling effect against cotton Verticillium wilt. However, its mechanism for controlling Verticillium wilt remains unclear. The objective of this study was to further clarify its controlling effect and mechanism against cotton Verticillium wilt. Results The results of confrontation culture test and double buckle culture test showed that the inhibitory effects of EBS03 volatile and nonvolatile metabolite on mycelium growth of Verticillium dahliae were 70.03% and 59.00%, respectively; the inhibitory effects of sporulation and microsclerotia germination were 47.16% and 70.06%, respectively. In the greenhouse test, the EBS03 fermentation broth root irrigation had the highest controlling effect at 87.11% on cotton Verticillium wilt, and significantly promoted the growth of cotton seedlings. In the field experiment, the controlling effect of EBS03 fermentation broth to cotton Verticillium wilt was 42.54% at 60 days after cotton sowing, and the boll number per plant and boll weight in EBS03 fermentation broth seed soaking, root irrigation, and spraying treatments significantly increased by 19.48% and 7.42%, 30.90% and 2.62%, 15.99% and 9.20%, respectively. Furthermore, EBS03 improved the resistance of cotton leaves against the infection of V. dahliae, and induced the outbreak of reactive oxygen species and accumulation of callose. In addition, the results of real time fluorescent quantitative polymerase chain reaction (RT-qPCR) detection showed that EBS03 significantly induced upregulation expression level of defense-related genes PAL, POD, PPO, and PR10 in cotton leaves, enhanced cotton plant resistance to V. dahliae, and inhibited colonization level of this fungal pathogen in cotton. Conclusion Bacillus subtilis EBS03 has a good biological defense capability, which can inhibit the growth and colonization level of V. dahliae, and activate the resistance of cotton to Verticillium wilt, thus increase cotton yield.

Aims: Cotton continuous cropping might cause the soil-borne disease Verticillium wilt in agricultural production. Crop rotation has been shown to be an effective method for controlling this disease. The objectives of this study were to evaluate the effects of cotton-maize rotation (CMR) and cotton-maize-broccoli residues rotation (CMBR) on incidence of cotton Verticillium wilt (CVW), soil microbial community structure and function. Methods: qPCR was used to determine the populations of V. dahliae. Soil microbial community structure and function was analysed using metagenomics analysis. Results: Results demonstrated that the disease incidence significantly decreased by 87.19% and 98.93%, and the populations of V. dahliae decreased by 67.07% and 84.36% under the CMR and CMBR treatments, respectively. There were significantly differences in bacterial communities under the CMR and CMBR treatments. Redundancy analysis revealed that the structures of microbial communities were mainly driven by available potassium (AK) and pH. Spearman's correlation analysis showed that most of KEGG pathways in bacterial community were negatively with pH, positively with NH4+-N and AK. The abundances of NifH, AmoA, NirK, and NarG genes (involved in nitrogen cycling), PfkABC, FbaB, PGAM and KdpgA genes (carbon cycling), and Sir, Sat, and CysND (sulfur cycling) were enriched by the CMR and CMBR treatments. Conclusions: CMR and CMBR could change the bacterial community structure and nutrients cycling in cotton rhizosphere, decrease the populations of V. dahliae in soil, therefore significantly reduce the incidence of CVW. [ABSTRACT FROM AUTHOR]

Background: Cotton Verticillium wilt, causing by Verticillium dahliae, has seriously affected the yield and quality of cotton. The incidence of Verticillium wilt in cotton fields has been on the rise for many years, especially after straw has been returned to the fields. Intercropping can reduce the incidence of soil borne diseases and is often used to control crop diseases, but the relationship between the effects of intercropping on microbial communities and the occurrence of plant diseases is unclear. This research explored the relationship between soil microbial community structure and Cotton Verticillium wilt in interplanting of cotton-onion, cotton-garlic, cotton-wheat and cotton monocultures. Amplicon sequencing applied to the profile of bacterial and fungal communities. Results: The results showed that the disease index of Cotton Verticillium wilt was significantly reduced after intercropping with cotton-garlic and cotton-onion. Chao1 and Sobs indices were not significantly different in the rhizosphere soil and pre-plant soils of the four planting patterns, but the pre-plant fungal shannon index was significantly lower in the cotton-onion intercropping plot than in the other three plots. PCoA analysis showed that the soil microbial communities changed to a certain extent after intercropping, with large differences in the microbial communities under different cropping patterns. The abundance of Chaetomium was highest in the cotton-garlic intercropping before planting; the abundance of Penicillium was significantly higher in the cotton-wheat intercropping than in the other three systems. Conclusion: Cotton-garlic and cotton-onion interplanting can control Cotton Verticillium wilt by affecting the soil microbial community. Fungi of the genera Chaetomium and Penicillium may be associated with plant disease resistance. [ABSTRACT FROM AUTHOR]

Verticillium wilt (VW) is threatening global cotton yield and quality. However, the accurate spatio-temporal dynamics of cotton VW in large regions remain unclear, especially in the complicated oasis agriculture ecosystem. We explored the spatio-temporal distribution of VW in the cotton-dominated region of Northern Xinjiang characterized by a typical desert-oasis irrigation agriculture ecosystem, utilizing reconstructed high-resolution time series remote sensing data from 2019 to 2021. Results revealed that the area of VW-infected cotton remained consistently around 20 % each year. However, there were significant seasonal variations, with outbreaks peaking in August. Regarding the spatial pattern of cotton VW, the mean temperature (TMP) difference in irrigated cotton fields along the Gurbantunggut Desert-oasis gradient reaches a maximum of approximately 1 ℃ caused by warming effect of the desert on the oasis (WEDO) during the cotton growing period. This TMP difference drives the spatial pattern of cotton VW incidence characterized by a low-to-high-to-moderate trend. WEDO significantly suppresses the cotton VW incidence due to relatively higher TMP, forming the low-risk zone for cotton VW within 16 km near the Gurbantunggut Desert. Our study significantly enhances the understanding of the spatio-temporal dynamics of cotton VW in the distinctive oasis irrigation agricultural ecosystem and advances the accurate monitoring and assessment of cotton VW across large regions. [Display omitted] • The outbreak of cotton VW exhibits strong seasonal variations. • Temperature drives the spatial pattern of cotton VW in irrigated farmland. • WEDO significantly suppresses the cotton VW incidence. [ABSTRACT FROM AUTHOR]

Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the original spectra were processed using Savitzky–Golay (SG) smoothing, and on its basis through mean centering, standard normal variate (SG-SNV), multiplicative scatter correction (SG-MSC), reciprocal second-order differentiation, and logarithmic second-order differentiation ([lg(SG)]″) preprocessing operations. The characteristic bands were selected based on the correlation coefficient, vegetation index, successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS). The single-factor model, back propagation neural network of particle swarm optimization algorithm, and extreme learning machine (ELM) of a grey wolf optimizer (GWO) algorithm were constructed to compare and explore the ability of each model to estimate the soil plant analysis development (SPAD) value of cotton under VW stress. The results showed that spectral pretreatment could improve the correlation between characteristic bands and SPAD values. SG-MSC and SG-SNV showed better changes in the five pretreatments, and the maximum correlation coefficients of healthy and VW cotton leaves were higher than 0.74. Compared with SPA, the accuracy of model estimation based on CARS-extracted characteristic bands was higher, and the estimation accuracy of the multi-factor model was better than that of the single-factor model under each pretreatment. For healthy cotton leaves, [lg(SG)]″–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.956 and 0.887, respectively. For VW cotton leaves, SG-MSC–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.832 and 0.824, respectively. Therefore, the GWO–ELM model constructed under different pretreatments combined with characteristic extraction methods can be used for the estimation of leaf SPAD values under VW stress to dynamically monitor VW stress in cotton and provide a theoretical reference for precision agriculture.

Abstract Verticillium wilt is one of the most important diseases affecting cotton production in China. The fungus, Verticillium dahliae, has a wide host range and a high degree of genetic variability. No resistance resources have been found in the available planting resources, thus presenting difficulties and challenges for our study. The long-term production practice shows that selection of disease-resistant varieties is the most economical and effective measure to control Verticillium wilt of cotton to reduce the yield loss and quality decline of cotton. In this paper, we summarized the genetic mapping population, the analysis method of genetic localization, the discovery, mining and cloning of disease-resistant quantitative trait loci/markers, and the analysis of their genetic functions, so as to provide information for the molecular breeding approach of disease-resistant cotton.

In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease.

The plant microbiome plays a fundamental role in plant growth and health. However, detailed information regarding the plant endophytic microbiome during the infection period of a pathogen is largely unknown. Here, we investigated the microbial community of healthy and diseased cotton plants and the root exudate profiles of susceptible and resistant cultivars utilizing high-throughput sequencing and metabolomics. The results showed that the pathogen infection reduced bacterial diversity and significantly affected the bacterial community composition. The microbiome assembly is shaped predominantly by cultivars. The endophytic microbiome of the infected plants showed greater complexity than the healthy plants in network analysis. The results displayed that a total of 76 compounds were significantly different in the two groups, with 18 compounds showing a higher relative abundance in the resistant cultivars and 58 compounds in the susceptible cultivars. Pathway enrichment analysis showed that pathways related to plant hormone signal transduction, biosynthesis of various secondary metabolites, and biosynthesis and metabolism of amino acids were prominently altered. We also demonstrate that plants inoculated with Pseudomonas sp. strains showed increased resistance to the cotton Verticillium wilt compared with the control plants in pot experiments. Overall, it showed that the pathogen infection affected the community composition, and healthy plants displayed an enriched beneficial microbiome to combat the plant disease. These findings significantly advance our understanding of the endophytic microbiome assembly under the pathogen infection and develop microbiome-based solutions for sustainable crop production systems. [ABSTRACT FROM AUTHOR]

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pseudomonas nitroreducens L4 was isolated from the interior of cotton plants, which showed strong biocontrol activity against Verticillium dahlia and other fungal pathogens. To elucidate the biocontrol mechanism, the genome sequence of L4 was sequenced using the Illumina and Nanopore sequencing platform. The assembled genome of L4 consisted of a single circular chromosome was 6,229,472 bp, with an average GC content of 64.95 %, 5,629 protein-coding genes, 72 tRNA, 16 rRNA and 1 tm RNA. Six secondary metabolite biosynthetic gene clusters are identified in the genome. The genome sequence provided a theoretical basis for analyzing the biocontrol mechanism of this strain., (© 2024 The Authors.)

Cotton Verticillium wilt caused by Verticillium dahliae (V. dahliae) is one of the most destructive fungal diseases and is difficult to control. However, resistant germplasm resources are scarce in cotton. Many studies have shown that host-induced gene silencing (HIGS) is a practical and effective technology in crop disease prevention by silencing virulence genes of pathogens. Acetolactate synthase (ALS) contains a catalytic subunit ILV2 and a regulatory subunit ILV6, which catalyzes the first common step reaction in branched-chain amino acid (BCAA) biosynthesis. We identified two acetolactate synthases, VdILV2 and VdILV6, which are homologs of ILV2 and ILV6, respectively, in Magnaporthe oryzae. To characterize the function of VdILV2 and VdILV6 in V. dahliae , we suppressed their expression in the strong pathogenic isolate Vd991 by using HIGS technology. VdILV2- or VdILV6- silenced V. dahliae had a dramatic reduction in pathogenicity. The results indicated that VdILV2 and VdILV6 are involved in the pathogenicity of V. dahliae. HIGS of VdILV2 or VdILV6 provides a novel fungicide target and an effective control to resist Verticillium wilt caused by V. dahliae. • Cotton Verticillium wilt caused by Verticillium dahliae (V. dahliae) is one of the most destructive fungi diseases. • HIGS of VdILV2 and VdILV6 enhanced cotton resistance to Verticillium wilt. • VdILV2 and VdILV6 are involved in pathogenicity in V. dahliae. • HIGS of VdILV2 or VdILV6 provides an effective control to resist Verticillium Wilt caused by V. dahliae. [ABSTRACT FROM AUTHOR]

• Three features were extracted to estimate Cotton Verticillium wilt disease index (DI). • Compared to color index and texture, vegetation index was significantly correlated with DI. • The introduction of normalized difference texture index combined with two textures can improve the accuracy of DI estimation. • Collaborative modeling of multiple sources of data to estimate DI has yielded good results. Verticillium wilt seriously jeopardizes cotton growth and restricts cotton yields. Therefore, it is important to accurately, rapidly, and non-destructively estimate the extent of cotton Verticillium wilt (CVW). The focus of this study was to explore the potential of combining the vegetation index (VI), color index (CI), and texture features to improve the accuracy of CVW disease severity estimation based on hexacopter Unmanned Aerial Vehicle (UAV) images. Simple Linear Regression (LR) and Multiple Linear Regression (MLR) methods were used to determine correlations between VI, CI, texture, and normalized difference texture index (NDTI) variables and cotton Verticillium wilt disease index (DI). The LR model based on VI, CI, and NDTI was constructed, VIs, CIs, and NDTIs were fused, and Grey Wolf Optimizer (GWO) Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) Backpropagation Neural Network (BP) models were constructed to comparatively explore the estimation ability of each model on the degree of CVW disease. The results showed that VI was significantly correlated with DI, followed by NDTI, and CI. Compared with texture, NDTI containing both texture features was more significantly correlated with DI. The accuracy of the DI estimation using LR was highest for the one-factor VI model (R2 > 0.48, RRMSE < 51.48), followed by the NDTI model (R2 > 0.47, RRMS < 60.81) and the CI model (R2 > 0.33, RRMSE < 52.58). The PSO-BP and GWO-ELM were further used to model the DI estimation with different input variables. Regardless of the period, the fusion of three data sources (VIs + CIs + TIs) was preferable to a single data source or a combination of two data sources for different model inputs. In terms of different modeling algorithms, GWO-ELM combining VIs, CIs, and NDTIs had the highest estimation accuracy compared with SR and PSO-BP, with a validated R2 values of 0.65 (RRMSE = 42.96) at the flowering stage, 0.66 (RRMSE = 20.00) at the flower and boll stage, and 0.88 (RRMSE = 10.53) at the boll stage. This study demonstrated that the estimation accuracy of DI was significantly improved using collaborative modeling with multiple data sources. This study provides ideas and methods for monitoring crop disease conditions using low-altitude remote sensing. [ABSTRACT FROM AUTHOR]

Abstract The large-scale long-term plantation of cotton in the Xinjiang region has been accompanied by a regular and wide outbreak of soil-borne fungal diseases such as verticillium wilt, which significantly damaged the local cotton industry. High-throughput sequencing data showed that the cotton field cultivation management measures pose a significant influence upon the original ecological soil fungal community structure. During long-term continuous cropping of cotton, a new soil fungal community structure emerges after several repeated adjustments over five years. The number of verticillium wilt pathogens in the soil increased rapidly with prolonged continuous cropping time, reaching a maximum at around the 10th y; moreover, the abundance of the verticillium wilt pathogen only serves as one of numerous essential factors for disease occurrence. The fungal community structure and the abundance of verticillium wilt pathogens in local cotton fields are gradually formed under joint effects of year-long continuous cropping and supporting cultivation management measures. [ABSTRACT FROM AUTHOR]

Verticillium wilt is one of the most crucial diseases caused by Verticillium dahliae that threatens the cotton industry. Statistical results showed that the return of cotton plants infected with V. dahliae to the field might be an essential cause of the continuous aggravation of cotton Verticillium wilt. The correlation among the cotton plants infected with V. dahliae returning to the field, the occurrence of Verticillium wilt, and the number of microsclerotia in rhizosphere soil need further investigation. A potted experiment was carried out to explore the effects of the direct return of cotton plants infected with Verticillium dahliae to the field on the subsequent growth and Verticillium wilt occurrence in cotton. As a risk response plan, we investigated the feasibility of returning dung-sand (i.e., insect excreta) to the field, the dung-sand was from the larvae of Protaetia brevitarsis (Coleoptera: Cetoniidea) that were fed with the V. dahliae -infected cotton plants. The results demonstrated that the return of the entire cotton plants to the field presented a promotional effect on the growth and development of cotton, whereas the return of a single root stubble or cotton stalks had an inhibitive effect. The return of cotton stalks and root stubble infected with V. dahliae increased the risk and degree of Verticillium wilt occurrence. The disease index of Verticillium wilt occurrence in cotton was positively correlated with the number of microsclerotia in the rhizosphere soil. The disease index increased by 20.00%, and the number of soil microsclerotia increased by 8.37 fold in the treatment of returning root stubble infected with V. dahliae to the field. No Verticillium wilt microsclerotia were detected in the feed prepared from cotton stalks and root stubble fermented for more than 5 days or in the transformed dung-sand. There was no risk of inoculation with Verticillium wilt microsclerotia when the dung-sand was returned to the field. The indirect return of cotton plants infected with V. dahliae to the field by microorganism-insect systems is worthy of further exploration plan of the green prevention and control for Verticillium wilt and the sustainable development of the cotton industry., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Meng, Ge, Yuan, Qiang, Jiang and Ma.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effects of three years continuous application of different rates of bio-organic fertilizer (BOF) were evaluated on three kinds of the counts of soil microbes, microbial metabolic activity and disease index of verticillium wilt through greenhouse pot experiments. The results showed that, the BOF not only reduced the occurrence of verticillium wilt in cotton and decreased the counts of Verticillium dahliae, but also improved the number of fungi, bacteria, actinomycetes, and microbial activity. The number of V. dahliae reduced gradually and fungi, bacteria, actinomycetes and average well color development (AWCD) firstly increased and then decreased with increasing the amount of fertilization in different organic matter content of soil. The number of V. dahliae was significantly increased by three years of fertilization. It showed that application of bio-organic fertilizer only delays the growth in the number of pathogen in soil within a certain time. The numbers of bacteria, actinomycetes were increased and the number of fungi was slightly reduced with increase of fertilization period. Application of bio-organic fertilizer made AWCD significantly higher than no fertilization (CK). By cluster analysis and principal component analysis, the results showed that the classification similar to disease index of various treatments, and it coincided with the number of microorganisms and AWCD values. BOF not only can control and improve the metabolic characteristics of soil microbial communities, but also maintain a high soil biological activity. [ABSTRACT FROM AUTHOR]

• B. subtilis strain NCD-2 is a biocontrol agent for cotton verticillium wilt. • Fengycin was main antifungal compound against V. dahliae. • Surfactin mainly affected the root colonization of strain NCD-2. • Both fengycin and surfactin contribute to the biocontrol efficiency. Bacillus subtilis strain NCD-2 is a promising biocontrol agent for soil borne plant diseases. A previous study indicated that strain NCD-2 could produce surfactin and fengycin simultaneously. In this study, the exact roles of surfactin and fengycin in strain NCD-2 against cotton verticillium wilt were determined by knockout of the surfactin synthetase gene srfAA and the fengycin synthetase gene fenC. Compared to the wild-type strain NCD-2, the fengycin-deficient mutant ΔFenC decreased the antifungal ability toward Verticillium dahliae , but the surfactin-deficient mutant ΔSrfAA mainly decreased the swarming and biofilm formation abilities of strain NCD-2. Comparatively, the fengycin and surfactin double mutant ΔFenC/SrfAA decreased the antifungal activity and swarming and biofilm formation abilities to more significant levels. The colonization abilities of the wild type strain NCD-2 and its derivative mutants were compared in the cotton rhizosphere. Compared to the wild-type strain NCD-2, ΔSrfAA and ΔFenC/SrfAA exhibited the poorest colonization ability. RT-qPCR results confirmed that knockout of the srfAA and fenC genes decreased the expression of biofilm formation-related genes. The biocontrol abilities of the strains against cotton verticillium wilt were evaluated in a greenhouse. All mutants decreased the biocontrol abilities against cotton verticillium wilt, while ΔFenC/SrfAA showed the poorest biocontrol ability, followed by ΔFenC. Taken together, fengycin was the main antifungal active compound, and surfactin affected root colonization. Both the antifungal ability and root colonization of strain NCD-2 played important roles in suppressing cotton verticillium wilt. [ABSTRACT FROM AUTHOR]

The plant microbiome plays a fundamental role in plant growth and health. However, detailed information regarding the plant endophytic microbiome during the infection period of a pathogen is largely unknown. Here, we investigated the microbial community of healthy and diseased cotton plants and the root exudate profiles of susceptible and resistant cultivars utilizing high-throughput sequencing and metabolomics. The results showed that the pathogen infection reduced bacterial diversity and significantly affected the bacterial community composition. The microbiome assembly is shaped predominantly by cultivars. The endophytic microbiome of the infected plants showed greater complexity than the healthy plants in network analysis. The results displayed that a total of 76 compounds were significantly different in the two groups, with 18 compounds showing a higher relative abundance in the resistant cultivars and 58 compounds in the susceptible cultivars. Pathway enrichment analysis showed that pathways related to plant hormone signal transduction, biosynthesis of various secondary metabolites, and biosynthesis and metabolism of amino acids were prominently altered. We also demonstrate that plants inoculated with Pseudomonas sp. strains showed increased resistance to the cotton Verticillium wilt compared with the control plants in pot experiments. Overall, it showed that the pathogen infection affected the community composition, and healthy plants displayed an enriched beneficial microbiome to combat the plant disease. These findings significantly advance our understanding of the endophytic microbiome assembly under the pathogen infection and develop microbiome-based solutions for sustainable crop production systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zeng, Man, Dai and Liu.)

• Bacillus subtilis NCD-2 (BS) decreased disease index of CVW, increased cotton yields. • BS did not decreased population of V. dahliae in rhizosphere. • BS increased bacterial and fungal diversities. • BS changed the microbial community structure. • Microbial communities structure was correlated with soil nutrients after BS applicaiton. Bacillus subtilis NCD-2 (BS) shows prominent biological control for cotton verticillium wilt (CVW). However, it is unknown whether the effects on the abundant and rare microbial communities in rhizosphere after application of BS. Therefore, we investigated the effects of BS treatment on the suppression of CVW and the microbial community structure in rhizosphere. BS treatment successfully suppressed CVW, achieving a control effect of 61.60%, and cotton yield increased by 6.07% compared with blank control. Moreover, application of BS resulted in 28.17%, 18.63%, and 5.08% increases in plant fresh weight, dry weight, and number of branches, respectively. However, the populations of Verticillium dahliae in rhizosphere did not decrease by BS treatment. Microbial diversity analysis showed that soil bacterial and fungal diversities were increased under BS treatment. Principal component analyses revealed that BS had significantly effect on the abundant and rare microbial communities. Higher abundances of Acidobacteria, Chloroflexi, and Planctomycetes were observed among abundant bacterial taxa in the BS treatment. Furthermore, the abundances of Arenimonas , Hydrogenophaga, Chaetomium , Aspergillus, Nectria, Psathyrella, Conocybe, and Chrysosporium were also significantly higher than those in blank control. Redundancy analysis indicated that the microbial communities structure in BS treatment were positively related to ammonium nitrogen (NH 4 +-N), inorganic phosphorus (IP), and pH, but negatively to nitrate nitrogen (NO 3 –-N), AK, and organic matter (OM). These research results may offer theoretical bases for comprehensive the mechanism of ecological prevention and control CVW by application of BS. [ABSTRACT FROM AUTHOR]