Disease Of Cucumber Research Articles

Bio-efficacy of Biochar's against Fusarium oxysporum f.sp. radicis-cucumerinum inciting Root and Stem Rot Disease of Cucumber in In vitro Condition

To assess in vitro evaluation of bio-efficacy different three biochar's viz., Green house waste (GHW), Eucalyptus wood (EW) and Citrus wood (CW) were evaluated at different three concentrations (each @ 1, 2 and 3%) against F. oxysporum f.sp. radicis-cucumerinum causing root and stem rot of cucumber by using poison food technique. Among the three different biochars, Green house waste (GHW) was found most effective by showing minimum mycelial growth of 50.78, 40.10 and 29.88 mm and 43.58, 55.44 and 66.80% per cent growth inhibition at 1, 2 and 3% concentrations, Whereas, Citrus wood (CW) depicted highest mycelial growth 66.26, 55.88, 44.30 mm with 26.38, 37.91 and 50.78% lowest per cent growth inhibition of the pathogen at same above concentrations, respectively.

In Vitro Antimicrobial Bio-efficacy Assessment of Different Organic Formulations against Root and Stem Rot Disease of Cucumber Incited by Fusarium oxysporum f.sp. radicis-cucumerinum

In vitro evaluation of antimicrobial bio-efficacy of different four organic formulations viz., Neem oil (@ 0.5, 1.0, 1.5% con.), Azadirachtin (@ 0.10, 0.15, 0.20% con.), Bijamrut (@ 5, 10, 15% con.) and Jivamrut (@ 5, 10, 15% con.) were evaluated at different three concentrations against Fusarium oxysporum f.sp. radicis-cucumerinum causing root and stem rot disease of cucumber. Among the four different organic formulations, Azadirachtin was found most effective by showing minimum mycelial growth of 47.48, 31.63 and 21.70 mm and 47.25, 64.86 and 75.89% per cent growth inhibition at 0.10, 0.15 and 0.20% concentrations respectively. The Jivamrut depicted highest mycelial growth 63.88, 55.83, 43.95 mm with 29.03, 37.97 and 51.17% lowest per cent growth inhibition of the pathogen at 5, 10 and 15% concentrations, respectively.

Small-sample cucumber disease identification based on multimodal self-supervised learning

It is difficult and costly to obtain large-scale, labeled crop disease data in the field of agriculture. How to use small samples of unlabeled data for feature learning has become an urgent problem that needs to be solved. The emergence of self-supervised contrastive learning methods and self-supervised mask learning methods can solve the problem of missing labels on the training data. However, each of these paradigms comes with its own advantages and drawbacks. At the same time, the features learned by dataset in a single modality are limited, ignoring the correlation with other modal information. Hence, this paper introduced an effective framework for multimodal self-supervised learning, denoted as MMSSL, to address the task of identifying cucumber diseases with small sample sizes. Integrating image self-supervised mask learning, image self-supervised contrastive learning, and multimodal image-text contrastive learning, the model can not only learn disease feature information from different modalities, but also capture global and local disease feature information. Simultaneously, the mask learning branch was enhanced by introducing a prompt learning module based on a cross-attention network. This module aided in approximately locating the masked regions in the image data in advance, facilitating the decoder in making accurate decoding predictions. Experimental results demonstrate that the proposed method achieves a 95% accuracy in cucumber disease identification in the absence of labels. The approach effectively uncovers high-level semantic features within multimodal small-sample cucumber disease data. GradCAM is also employed for visual analysis to further understand the decision-making process of the model in disease identification. In conclusion, the proposed method in this paper is advantageous for enhancing the classification accuracy of small-sample cucumber data in a multimodal, unlabeled context, demonstrating good generalization performance.

Evaluation of Bacillus velezensis F9 for Cucumber Growth Promotion and Suppression of Fusarium wilt Disease.

Cucumber wilt, caused by Fusarium oxysporum f. sp. cucumerinum (FOC), is a soilborne disease that poses a significant threat to cucumber production, resulting in substantial yield losses. This study aimed to evaluate the biocontrol and growth-promoting effects of Bacillus velezensis, a highly active bacterial strain. In vitro assays revealed that B. velezensis F9 exhibited broad-spectrum antifungal activity against eight plant pathogenic fungi, with inhibition ratio ranging from 62.66% to 88.18%. Additionally, the strain displayed the ability to produce IAA (5.97 ± 1.75 µg/mL), fix nitrogen, produce siderophores, and form biofilms. In vitro growth promotion assays demonstrated that different concentrations of B. velezensis F9 significantly promoted cucumber seedling growth. Furthermore, two pot experiments revealed that the strain exhibited biocontrol efficacy against cucumber wilt, with disease control rates ranging from 42.86% to 67.78%. Notably, the strain significantly increased the plant height, fresh weight, and dry weight, with increases ranging from 20.67% to 60.04%, 40.27% to 75.51%, and 22.07% to 52.54%, respectively. Two field trials confirmed the efficacy of B. velezensis F9 in controlling cucumber wilt, with disease control rates of 44.95% and 33.99%, respectively. The strain effectively alleviated the dwarfing and wilting symptoms caused by the pathogen. Compared with the FOC treatment, the F9 + FOC treatment significantly increased the plant height, fresh weight, and dry weight, with increases of 43.85% and 56.28%, 49.49% and 23.70%, and 36.25% and 73.63%, respectively. Enzyme activity assays indicated that inoculation significantly increased SOD activity in cucumber leaves and neutral phosphatase, sucrase, and urease activity in rhizosphere soil. Correlation analysis revealed a negative correlation between the disease index and plant height, fresh weight, dry weight, and peroxidase activity, with correlation coefficients of -0.53, -0.60, -0.38, and -0.45, respectively. These findings suggest that plant height, fresh weight, and dry weight are significantly negatively correlated with the cucumber disease index, highlighting their importance as indicators for evaluating the biocontrol efficacy of B. velezensis F9. In conclusion, B. velezensis F9 is a highly effective plant growth-promoting rhizobacterium with excellent biocontrol potential, showcasing promising applications in agricultural production.

Integration of Diffusion Transformer and Knowledge Graph for Efficient Cucumber Disease Detection in Agriculture.

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model's ability to recognize complex agricultural disease features and to address the issue of sample imbalance efficiently. Experimental results demonstrate that the proposed method outperforms existing deep learning models in cucumber disease detection tasks. Specifically, the method achieved a precision of 93%, a recall of 89%, an accuracy of 92%, and a mean average precision (mAP) of 91%, with a frame rate of 57 frames per second (FPS). Additionally, the study successfully implemented model lightweighting, enabling effective operation on mobile devices, which supports rapid on-site diagnosis of cucumber diseases. The research not only optimizes the performance of cucumber disease detection, but also opens new possibilities for the application of deep learning in the field of agricultural disease detection.

Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features

Cucumber (Cucumis sativus) is a significant horticultural crop worldwide, highly valued for both fresh consumption and processing. However, cucumber cultivation faces challenges due to diseases that can substantially reduce yield and quality. Diseases like leaf spots, stem wilt, and fruit rot are caused by pathogens including viruses, bacteria, and fungi. Traditionally, disease detection in cucumbers is performed manually, which is time-consuming and inefficient. Therefore, developing machine vision-based models using Deep Learning (DL) and Machine Learning (ML) for early disease detection through image analysis is crucial for assisting farmers. While many studies on plant disease classification using various DL and ML models show optimal results, research on cucumbers has mostly focused on leaf diseases. This study aims to optimize cucumber disease image classification by developing a model that combines Local Binary Pattern (LBP) texture features and VGG-16 convolutional features. The dataset used, Cucumber Disease Recognition Dataset consists of 8 classes of cucumber plant disease images covering leaves, stems, and fruits. This study classifies cucumber plant disease images using Random Forest (RF) combined with LBP texture features and VGG-16 visual features and compares its performance with models using VGG-16, LBP+RF, and VGG-16+RF on the same dataset. The results show that the proposed model achieved a precision of 84.7%, recall of 84%, F1-Score of 83.8%, and accuracy of 84%. These results outperform the comparative models, demonstrating the effectiveness of the combined approach in classifying cucumber plant diseases.

Adaptive loss-guided multi-stage residual ASPP for lesion segmentation and disease detection in cucumber under complex backgrounds

BackgroundIn complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction.ResultsAn original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas.ConclusionsThe LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions.

Joint metabolomic and transcriptomic analysis identify unique phenolic acid and flavonoid compounds associated with resistance to fusarium wilt in cucumber (Cucumis sativus L.).

Fusarium wilt (FW) caused by Fusarium oxysporum f. sp. cucumerinum (Foc) is a destructive soil-borne disease in cucumber (Cucumis sativus. L). However, there remains limited knowledge on the molecular mechanisms underlying FW resistance-mediated defense responses in cucumber. In this study, metabolome and transcriptome profiling were carried out for two FW resistant (NR) and susceptible (NS), near isogenic lines (NILs) before and after Foc inoculation. NILs have shown consistent and stable resistance in multiple resistance tests conducted in the greenhouse and in the laboratory. A widely targeted metabolomic analysis identified differentially accumulated metabolites (DAMs) with significantly greater NR accumulation in response to Foc infection, including many phenolic acid and flavonoid compounds from the flavonoid biosynthesis pathway. Transcriptome analysis identified differentially expressed genes (DEGs) between the NILs upon Foc inoculation including genes for secondary metabolite biosynthesis and transcription factor genes regulating the flavonoid biosynthesis pathway. Joint analysis of the metabolomic and transcriptomic data identified DAMs and DEGs closely associated with the biosynthesis of phenolic acid and flavonoid DAMs. The association of these compounds with NR-conferred FW resistance was exemplified by in vivo assays. These assays found two phenolic acid compounds, bis (2-ethylhexyl) phthalate and diisooctyl phthalate, as well as the flavonoid compound gallocatechin 3-O-gallate to have significant inhibitory effects on Foc growth. The antifungal effects of these three compounds represent a novel finding. Therefore, phenolic acids and flavonoids play important roles in NR mediated FW resistance breeding in cucumber.

Effect of Bio Control Agents on Controlling Root Rot and Damping-off Disease of Cucumber Seedlings Caused by Rhizoctonia solani

Root rot and seedlings damping-off disease caused by Rhizoctonia solani is the most damaging disease of cucumber. This disease causes reducing yield in cucumber. This study conducted laboratory and greenhouse conditions to find out the efficacy of biological control agents, Trichoderma harzianum, Bacillus subtilis, the chemical compound Bion, and fungicide revanol. The results of pathogenicity test of R. solani on cucumber seeds showed that R. solanihas led to a significant reduction in the percentage of germination and seed rot compared to the control treatment, which amounted to 100%. The results showed the ability of all control agents used in this study to reduce the incidence of the disease. The Bion (benzothiadiazole) treatment showed significant effect and outperformed the other treatments in increasing the activity of the peroxidase enzyme, which was recorded at 14,440 (unit. minute-1.g wet weight-1). It also achieved the highest significant increase in total phenolics 1.52 (mg -1 . gram wet weight -1). Biological control agent T. harzianum has outperformed on other treatments in causing an increase in the activity of the polyphenol oxidase enzyme, it has recorded 1811.7 (unit. minute - 1 .gram of wet weight - 1 leaf).

In vitro antifungal activities of dose-dependent bioactive compound from potato peels to combat powdery mildew disease in cucumber

The search for biopolymer-based materials to replace dangerous chemicals in agriculture has become increasingly important in recent years for sustainable crop development. In our study enzymatic synthesis of isomaltose was done using potato peels. Isomaltose solvent extracts were tested for their ability to inhibit fungal pathogens like Erysiphe cichoracearum and Fusarium oxysporum. Among those tested, a dosedependent isomaltose extract exhibited significant inhibition on E. cichoracearum and F. oxysporum pathogen conidial sporulation, number of conidia and their motility. Under the in vitro studies, with isomaltose extract at (2.5 mg mL ) recorded a maximum conidial inhibition of 86.2, ?1 E. cichoracearum average number of conidia 4-27 and 5 per cent motility. While, sterile distilled water and DMSO showed maximum growth of the both the pathogens and also their motility was 92-97 per cent, respectively. Similarly, F. oxysporum maximum conidial inhibition of 88.3 and average conidia of 9-30 with motility of 7 per cent was observed after treating with 2.5 mg mL isomaltose. Moreover, isomaltose a biopolymer ?1 derived from potato peel was used in different concentrations as priming agent to cucumber seeds and were evaluated for its effectiveness to enhance plant growth parameters. Among the treatments, seeds-primed with 2.5 mg mL of isomaltose extracts in water exhibited early seedling germination of 96.8 per cent and ?1 vigor of 2210.3 and also cucumber seeds treated with 2.5 mg mL of isomaltose extracts in DMSO showed ?1 significant germination rate of 95.5 per cent and vigor index of 1987.7. Overall, these results determine that the antifungal property of water dissolved isomaltose is highly significant in exhibiting remarkable inhibitory action against Fusarium and powdery mildew phytopathogens, further the dose-dependent concentration of water dissolved isomaltose showed fair to good enhancement of seed germination which will lead to establishing a novel bio-waste agricultural formulation for the management of plant pathogens and in particular Fusarium and powdery mildew disease of cucumber and other cucurbitaceous crops.. KEYWORDS :Bio-polymer, optimize, pathogen inhibition, plant growth, seed treatment

Cucumber disease classification using Transfer Learning

Cucumber disease classification using Transfer Learning

Comparative genomics, pangenomics, and phenomic studies of Pectobacterium betavasculorum strains isolated from sugar beet, potato, sunflower, and artichoke: insights into pathogenicity, virulence determinants, and adaptation to the host plant.

Bacteria of genus Pectobacterium, encompassing economically significant pathogens affecting various plants, includes the species P. betavasculorum, initially associated with beetroot infection. However, its host range is much broader. It causes diseases of sunflower, potato, tomato, carrots, sweet potato, radish, squash, cucumber, and chrysanthemum. To explain this phenomenon, a comprehensive pathogenomic and phenomic characterisation of P. betavasculorum species was performed. Genomes of P. betavasculorum strains isolated from potato, sunflower, and artichoke were sequenced and compared with those from sugar beet isolates. Metabolic profiling and pathogenomic analyses were conducted to assess virulence determinants and adaptation potential. Pathogenicity assays were performed on potato tubers and chicory leaves to confirm in silico predictions of disease symptoms. Phenotypic assays were also conducted to assess the strains ability to synthesise homoserine lactones and siderophores. The genome size ranged from 4.675 to 4.931 kbp, and GC % was between 51.0% and 51.2%. The pangenome of P. betavasculorum is open and comprises, on average, 4,220 gene families. Of these, 83% of genes are the core genome, and 2% of the entire pangenome are unique genes. Strains isolated from sugar beet have a smaller pangenome size and a higher number of unique genes than those from other plants. Interestingly, genomes of strains from artichoke and sunflower share 391 common CDS that are not present in the genomes of other strains from sugar beet or potato. Those strains have only one unique gene. All strains could use numerous sugars as building materials and energy sources and possessed a high repertoire of virulence determinants in the genomes. P. betavasculorum strains were able to cause disease symptoms on potato tubers and chicory leaves. They were also able to synthesise homoserine lactones and siderophores. The findings underscore the adaptability of P. betavasculorum to diverse hosts and environments. Strains adapted to plants with high sugar content in tissues have a different composition of fatty acids in membranes and a different mechanism of replenishing nitrogen in case of deficiency of this compound than strains derived from other plant species. Extensive phenomics and genomic analyses performed in this study have shown that P. betavasculorum species is an agronomically relevant pathogen.

Bioactive carbohydrate from potato peels: A sweet inducer that mediate resistance against powdery mildew disease of cucumber through phytohormonal alterations

Large volumes of nutritive wastes are generated by food industries, which can be used in the production of bioactive compounds of commercial values. The waste generated by potato chips industries contains high quantities of insoluble starch, high concentrations of total suspended solids and chemical oxygen demand, or environmental issues. In this study, the starch extracted from the potato peels was subjected to bioconversion of isomaltose-oligosaccharide by enzymatic method and further eluted isomatlose by liquid chromatography-mass spectrometry. The structural elucidation of isomaltose was confirmed by 1H NMR and 13C NMR spectroscopy. The purified isomaltose displayed excellent in vitro antifungal action against Erysiphe cichoracearum and Fusarium oxysporum at concentrations 1.0 and 2.5 mg/ml, respectively. Notably, cucumber-seeds primed with isomaltose (2.5 and 5.0 mg/mL) concentrations recorded a significant improvement of germination (91.5 and 89 %) and vigor (2210.93 and 2015.7) over the untreated control. Subsequently, the isomaltose-primed cucumber plants challenged with powdery mildew pathogen showed enhanced protection of 63% in comparison with pathogen alone-induced plants. This enhanced growth parameters and induced resistance in cucumber may be fairly contributed by the upregulation of phytohormone levels in primed plants observed in this study. Taken together, this study is unique in the use of bioactive isomaltose from potato peels, which could enable us to minimize the agro-industrial wastes and most importantly could help to develop new formulations for sustainable crop protection strategies.

DIAGNOSIS OF CUCUMBER PLANT DISEASES USING CERTAINTY FACTOR AND FORWARD CHAINING METHODS

Cucumber plants spread and can live in tropical climates like Indonesia. The cucumber plant has many benefits and can be a beauty ingredient. Cucumbers, like other plants, can also have disease attacks, which can threaten farmers. This expert system can help farmers discover diseases that attack cucumber plants and how to control them. The certainty Factor is a method used to measure the certainty of facts to describe an expert's confidence in facing a problem. Forward Chaining is an approach method monitored by data starting from information in the form of facts and supported by rules to reach conclusions. Implementing an expert system for diagnosing cucumber diseases using certainty factor and forward chaining methods will make it easier for farmers and the public to cultivate cucumber plants and get good results. Applying the forward chaining method and factor certainty in this expert system can produce an accuracy level of 95.918%.

A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet

Accurately estimating the severity of cucumber diseases is crucial for improving cucumber quality and minimizing economic losses. Deep learning techniques have shown promising results in automatically extracting disease image features for severity estimation. However, existing methods still face challenges in accurately estimating disease severity under complex backgrounds and achieving real-time performance.This paper presents a lightweight severity estimation method called DM-BiSeNet to address these challenges. The proposed method utilizes BiSeNet V2 as the base network and incorporates depthwise separable convolutional blocks to optimize the detail branch. A simplified MobileNet V3 network is also constructed to optimize the semantic branch. The model training process is accelerated using the AdamW optimizer. To evaluate the performance of DM-BiSeNet, a dataset consisting of cucumber powdery mildew and downy mildew disease images collected in natural scenes is utilized. Experimental results demonstrate that DM-BiSeNet achieves higher accuracy in severity estimation, with an R2 value of 0.9407 and an RMSE of 1.0680, outperforming the comparison methods. Moreover, DM-BiSeNet exhibits a complexity of 1.54 GFLOPs and is capable of reasoning 94 disease images per second.The proposed DM-BiSeNet model offers a lightweight and effective solution for accurate and rapid severity estimation of cucumber diseases under complex backgrounds. It provides a valuable technical tool for quantitative disease estimation, offering significant potential for practical applications.

Effect of probiotic feed supplementation on skin ulceration syndrome in Apostichopus japonicus

Skin ulceration syndrome is among the most harmful diseases in sea cucumber (Apostichopus japonicus). The annual loss due to this disease is more than 1 billion dollars, which seriously affected the healthy and sustainable development of A. japonicus. In this paper, animals were divided into 4 experimental groups: a control group given basic feed, experimental groups with 1% Lactobacillus, 1% Bacillus, 1% mixture with 0.5% Lactobacillus and 0.5% Bacillus added to the basic feed. After feeding for 60 days, the samples were taken to analyze the growth performance, digestive enzyme activity, and immune indexes of A. japonicus. Furthermore, the resistance to the skin ulceration syndrome of A. japonicus was analyzed. The results showed that the probiotics promoted the growth performance of A. japonicus, the weight gain rate of the control group was 120.08±12.07%, the weight gain rate of the Lactobacillus group was 152.11±10.06%, the weight gain rate of the Bacillus group was 148.03±9.88%, and the weight gain rate of the mixed bacteria group was as high as 210.11±19.41%. The digestive enzyme activities in the intestinal tract of A. japonicus increased significantly in the probiotic-added groups, and the mixed bacteria were more effective in enhancing the digestive enzyme activities than single Bacillus and Lactobacillus. In addition, the probiotics could significantly increase the phagocytosis rate of the body cavity cells of A. japonicus. The phagocytic rate of Lactobacillus and Bacillus subtilis was comparable; the mixed probiotics were significantly higher than those of single probiotics, and this phagocytic rate gradually increased over time. After Vibrio splendidus and Pseudoalteromonas infection, the mortality rate of A. japonicus in the probiotic-added group was reduced, which indicated that probiotics could improve the immunity of A. japonicus, and the ability to resist the infection of Vibrio splendidus and Pseudoalteromonas was enhanced. Bacillus exhibits superior antimicrobial ability compared to Lactobacillus, and the mixed strain displays the most effective antimicrobial properties. This study provided a scientific basis for effectively preventing and controlling skin ulceration syndrome in the aquaculture of A. japonicus.

Cucumber breeding for spring film greenhouses for resistance to real powdery mildew

Relevance. Powdery mildew is one of the most common and harmful diseases of cucumber (Cucumis sativus L.) in open and protected ground. Moreover, in the closed ground, the harmfulness is higher than in the field. The yield of cucumber, due to the defeat of plants by powdery mildew, decreases by 30-50%, and in some cases by more than 70%. The creation of modern cucumber hybrids is impossible without the constant involvement in the breeding process of new sources of economically useful traits, including resistance to real powdery mildew. It is very important to combine in one line both a high degree of parthenocarpy, and the female type of flowering, and resistance to the most harmful diseases. We tried to solve this problem in our work.Materials and methods. The experience was laid in 2018-2022 in the Odintsovo district of the Moscow region on the basis of the head institution of the Federal State Budgetary Scientific Institution Federal Scientific Vegetable Center (FSBSI FSVC) in the conditions of a spring ground greenhouse of the Block type, on a natural infectious background. On the basis of the laboratory of breeding and seed production of pumpkin crops, 27 collectible and about 50 breeding samples of parthenocarpic cucumber were studied. The article presents an assessment of the resistance to this powdery mildew of 36 of the most aligned and most valuable cucumber breeding samples in terms of a complex of economically useful features. 2-3 families, 6 plants in each, were evaluated for each sample for 2 years. During the entire growing season, the intensity of powdery mildew damage was assessed on a four-point scale and the spread of the disease in %.Results. Among the collection samples of cucumber, on a natural infectious background, two hybrids of the agrofirm BEJO ZADEN B.V. were not affected by powdery mildew: Amur 1801 F1, Artist F1, a hybrid without a name of the agrofirm MONSANTO HOLLAND B.V., conventionally named – Semenis F1 and hybrid Kibria F1 of the agrofirm RIJK ZWAAN. To a small extent, less than one point, they were amazed: Orpheus F1, Merengue F1, Marinda F1, Masha F1, Othello F1, List F1, Adam F1, SV 4097 F1 and Krasotka F1. 11cucumber breeding samples selected in previous years of research on a complex of economically useful signs that were not affected by this disease during 2 years of research were identified. Work will continue to evaluate these lines for resistance to real powdery mildew on an artificial infectious background.

Long-Term Field Application of a Plant Growth-Promoting Rhizobacterial Consortium Suppressed Root-Knot Disease by Shaping the Rhizosphere Microbiota.

Root-knot nematodes (Meloidogyne spp.) are one of the most economically important plant parasitic nematodes, infecting almost all cultivated plants and resulting in severe yield losses every year. Plant growth-promoting rhizobacteria (PGPR) have been extensively used to prevent and control root-knot diseases and increase yield. In this study, the effect of a consortium of three PGPR strains (Bacillus cereus AR156, B. subtilis SM21, and Serratia sp. XY21; hereafter "BBS") on root-knot disease of cucumber was evaluated. The application of BBS significantly reduced the severity of root-knot disease by 56 to 72%, increased yield by 36 to 55%, and improved fruit quality by 14 to 90% and soil properties by 1 to 90% relative to the control in the cucumber fields of the Nanjing suburb, Jiangsu Province, from 2015 to 2018. BBS altered the rhizosphere bacterial community. Compared with the control group, it significantly (false discovery rate, P < 0.05) increased the abundance of 14 bacterial genera that were negatively correlated with disease severity. Additionally, the redundancy analysis suggested that BBS-treated rhizosphere soil samples were dominated by disease-suppressive bacteria, including the genera Iamia, Kutzneria, Salinibacterium, Mycobacterium, Kribbella, Pseudonocardia, Sporichthya, Sphaerisporangium, Actinomadura, Flavisolibacter, Phenylobacterium, Bosea, Hyphomicrobium, Agrobacterium, Sphingomonas, and Nannocystis, which were positively related to total organic carbon, total nitrogen, total organic matter, dissolved organic carbon, [Formula: see text]-N, and available phosphorus contents. This suggests that BBS suppresses root-knot nematodes and improves the soil chemical properties of cucumber by altering the rhizosphere microbial community.

Conventional and Biopesticide Fungicides for Cucurbit Downy Mildew Control on Cucumber in Michigan

Michigan ranks first in the nation in cucumber production for processing (pickling), contributing over $45 million to the state's economy in 2019. Cucurbit downy mildew, caused by the oomycete Pseudoperonospora cubensis, is the most important disease of cucumber and an annual threat to yields. Fungicides are the most important means to control P. cubensis. Our goal was to monitor currently labelled conventional fungicides and biopesticides for efficacy over the course of an entire cropping period under high pathogen pressure. Field trials were conducted at the Michigan State University Plant Pathology Research Farm in 2021 and 2022. Fungicides were applied weekly, and disease severity was evaluated by visually assessing the percentage of foliar area showing downy mildew symptoms. At the end of the season, the relative area under the disease progress curve (rAUDPC) was calculated using the disease severity data. Based on rAUDPC results, oxathiapiprolin premixed with chlorothalonil was the most effective fungicide at controlling P. cubensis and was significantly better than all other conventional fungicides in both years tested. In both years, all conventional treatments had a significantly lower rAUDPC than the untreated control except pyraclostrobin, dimethomorph, and fluopicolide. No biopesticide fungicide reduced the rAUDPC compared with the untreated control in either year evaluated.

The mechanisms of target and non-target resistance to QoIs in Corynespora Cassiicola

Corynespora leaf spot, caused by Corynespora cassiicola, is a foliar disease in cucumber. While the application of quinone outside inhibitors (QoIs) is an effective measure for disease control, it carries the risk of resistance development. In our monitoring of trifloxystrobin resistance from 2008 to 2020, C. cassiicola isolates were categorized into three populations: sensitive isolates (S, 0.01 < EC50 < 0.83 μg/mL), moderately resistant isolates (MR, 1.18 < EC50 < 55.67 μg/mL), and highly resistant isolates (HR, EC50 > 56.98 μg/mL). The resistance frequency reached up to 90% during this period, with an increasing trend observed in the annual average EC50 values of all the isolates. Analysis of the CcCytb gene revealed that both MR and HR populations carried the G143A mutation. Additionally, we identified mitochondrial heterogeneity, with three isolates carrying both G143 and A143 in MR and HR populations. Interestingly, isolates with the G143A mutation (G143A-MR and G143A-HR) displayed differential sensitivity to QoIs. Further experiments involving gene knockout and complementation demonstrated that the major facilitator superfamily (MFS) transporter (CcMfs1) may contribute to the disparity in sensitivity to QoIs between the G143A-MR and G143A-HR populations. However, the difference in sensitivity caused by the CcMfs1 transporter is significantly lower than the differences observed between the two populations. This suggests additional mechanisms contributing to the variation in resistance levels among C. cassiicola isolates. Our study highlights the alarming level of trifloxystrobin resistance in C. cassiicola in China, emphasizing the need for strict prohibition of QoIs use. Furthermore, our findings shed light on the occurrence of both target and non-target resistance mechanisms associated with QoIs in C. cassiicola.