Disease Of Tomato Research Articles

Tomato bacterial wilt disease outbreaks are accompanied by an increase in soil antibiotic resistance

The presence of soil-borne disease obstacles and antibiotic resistance genes (ARGs) in soil leads to serious economic losses and health risks to humans. One area in need of attention is the evolution of ARGs as pathogenic soil gradually develops, which introduces uncertainty to the dynamic ability of conventional farming models to predict ARGs. Here, we investigated variations in tomato bacterial wilt disease accompanied by the resistome by metagenomic analysis in soils over 13 seasons of monoculture. The results showed that the abundance and diversity of ARGs and mobile genetic elements (MGEs) exhibited a significant and positive correlation with R. solanacearum. Furthermore, the binning approach indicated that fluoroquinolone (qepA), tetracycline (tetA), multidrug resistance genes (MDR, mdtA, acrB, mexB, mexE), and β-lactamases (ampC, blaGOB) carried by the pathogen itself were responsible for the increase in overall soil ARGs. The relationships between pathogens and related ARGs that might underlie the breakdown of soil ARGs were further studied in R. solanacearum invasion pot experiments. This study revealed the dynamics of soil ARGs as soil-borne diseases develop, indicating that these ecological trends can be anticipated. Overall, this study enhances our understanding of the factors driving ARGs in disease-causing soils.

Evaluation of Integrated Pest and Disease Management Combinations against Major Insect Pests and Diseases of Tomato in Tamil Nadu, India

Tomatoes are one of the predominant vegetable crops grown throughout the year in Tamil Nadu, India. Their perishable nature and resource-intensive cultivation make them susceptible to biotic stress. The damage caused by invasive insect pests, bacterial wilt during the rainy season, and viral diseases are major yield-limiting factors, and the farmers mostly depend on calendar-based insecticide applications for insect pest and disease management in tomatoes. The desired tomato hybrids grafted onto bacterial wilt-resistant eggplant rootstocks offer protection against bacterial wilt during the rainy season. The integrated pest and disease management (IPDM) practices consist of resistant grafted tomato seedlings (wild eggplant rootstocks EG 203 and TS 03), bioinoculants (Bacillus subtilis + Trichoderma asperellum + Purpureocillium lilacinum), pheromone traps (Phthorimaea absoluta and Helicoverpa armigera), botanicals (azadirachtin), microbial pesticides (Bacillus thuringiensis, Metarhizium anisopliae, and Beauveria bassiana), and bio-rationals, which were evaluated in four locations in two major tomato-growing tracts of Tamil Nadu. The results revealed that the treatment EG 203 eggplant rootstock-grafted tomato along with IPDM practices performed better across all experimental locations than the other treatment combinations viz., TS 03 eggplant rootstock-grafted tomato + IPDM, tomato + IPDM, grafted tomato + farmers’ practice and tomato + farmers’ practice. The EG 203-grafted tomato recorded a higher yield than the farmers’ practice with significantly superior biometric parameters. The treatment of EG 203-grafted tomato and IPDM practices can be adopted for safer tomato production by enabling a reduction in pesticide applications while enhancing productivity.

Impact of biosynthesized silver nanoparticles (AgNPs) on fusarium wilt disease in tomato: In vitro and In vivo studies Hafez, M. S., Abdou, El-S., Shaat, M. M. and Abd ElGawad, T. I. Plant Pathology Department, Faculty of Agriculture, Minia Universit

Impact of biosynthesized silver nanoparticles (AgNPs) on fusarium wilt disease in tomato: In vitro and In vivo studies Hafez, M. S., Abdou, El-S., Shaat, M. M. and Abd ElGawad, T. I. Plant Pathology Department, Faculty of Agriculture, Minia Universit

Tomato leaf disease detection and management using VARMAx-CNN-GAN integration

In contemporary agriculture, farmers confront substantial challenges in maintaining crop yields and mitigating agricultural losses attributable to diseases. The existing methods for diagnosing and managing tomato leaf diseases often exhibit deficiencies in terms of accuracy, robustness, and interpretability. Typically, these methods are reactive, addressing symptoms after the disease has already impacted the plants, resulting in delayed and often ineffective interventions. The precision of disease localization and severity estimation plays a crucial role in efficient disease treatment; regrettably, existing post-processing techniques frequently fall short in this regard. While these methods have their flaws, our proposed method uses the best parts of deep learning and vector autoregressive moving average processes with eXogenous regressors (VARMAx processes) to quickly and accurately find tomato leaf diseases. Our approach represents an innovative solution to the challenges currently confronting the agriculture sector, thanks to its proactive attributes, improved categorization capabilities, and advanced post-processing stages. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), built upon the “VARMAx-CNN-GAN Integration” framework, form the core of our method. In this integrated model, convolutional neural networks serve the purpose of extracting features and performing early disease classification, whereas generative adversarial networks come into play for generating synthetic images, expanding the dataset, and enhancing the model’s ability to generalize. The “VARMAx-CNN-GAN Integration” model improves disease classification and decision-making for farmers and agronomists by providing insights into critical leaf images. Compared to traditional methods, it improves precision, accuracy, recall, AUC, and delay in identifying tomato diseases. The approach also shows potential for disease prevention, revolutionizing tomato leaf disease identification and management.

Revealing bacteriophage capabilities: pH and NaCl concentration effects on RSJ2 phage infectivity and stiffness

The urgent necessity of reducing pesticides in the agricultural sector compels us to explore alternative, environmentally friendly approaches for controlling pathogenic bacteria and crop diseases. The usage of bacterial viruses or bacteriophages (phages) to control pathogenic bacteria is still limited due to the lack of understanding of the factors influencing shelf life. We examined the effects of pH and NaCl on the storage buffers on the infectivity of the RSJ2 phage. The RSJ2 phage infects Ralstonia solanacearum, causing bacterial wilt disease in tomatoes. The buffer that retained the highest phage titer (63 % after two months) was at pH 8.3 and 0.5 M NaCl. pH and NaCl concentrations directly affected the phage particles. The phage stiffness was measured with the nanoindentation technique, and a correlation between the phage remaining titers and the stiffness was revealed. The average stiffness associated with the highest remaining titer was 0.07 N/m, which falls into the intermediate stiffness range. The results agree with the previous study on the C22 phage stiffness, revealing an association between intermediate stiffness and phage infectivity. Phage stiffness can become an attribute of phage metastability and can be used to predict the phage shelf life.

One stone two birds: Endophytes alleviating trace elements accumulation and suppressing soilborne pathogen by stimulating plant growth, photosynthetic potential and defense related gene expression

In the present investigation, we utilized zinc nanoparticles (Zn-NPs) and bacterial endophytes to address the dual challenge of heavy metal (HM) toxicity in soil and Rhizoctonia solani causing root rot disease of tomato. The biocontrol potential of Bacillus subtilis and Bacillus amyloliquefaciens was harnessed, resulting in profound inhibition of R. solani mycelial growth and efficient detoxification of HM through strong production of various hydrolytic enzymes and metabolites. Surprisingly, Zn-NPs exhibited notable efficacy in suppressing mycelial growth and enhancing the seed germination (%) while Gas chromatography-mass spectrometry (GC-MS) analysis unveiled key volatile compounds (VOCs) crucial for the inhibition of pathogen. Greenhouse trials underscored significant reduction in the disease severity (%) and augmented biomass in biocontrol-mediated plants by improving photosynthesis-related attributes. Interestingly, Zn-NPs and biocontrol treatments enhanced the antioxidant enzymes and mitigate oxidative stress indicator by increasing H2O2 concentration. Field experiments corroborated these findings, with biocontrol-treated plants, particularly those receiving consortia-mediated treatments, displayed significant reduction in disease severity (%) and enhanced the fruit yield under field conditions. Root analysis confirmed the effective detoxification of HM, highlighting the eco-friendly potential of these endophytes and Zn-NPs as fungicide alternative for sustainable production that foster soil structure, biodiversity and promote plant health.

Classification of tomato leaf images for detection of plant disease using conformable polynomials image features

Plant diseases can spread rapidly, leading to significant crop losses if not detected early. By accurately identifying diseased plants, farmers can target treatment only to the affected areas, reducing the number of pesticides or fungicides needed and minimizing environmental impact. Tomatoes are among the most significant and extensively consumed crops worldwide. The main factor affecting crop yield quantity and quality is leaf disease. Various diseases can affect tomato production, impacting both yield and quality. Automated classification of leaf images allows for the early identification of diseased plants, enabling prompt intervention and control measures. Many creative approaches to diagnosing and categorizing specific illnesses have been widely employed. The manual method is costly and labor-intensive. Without the assistance of an agricultural specialist, disease detection can be facilitated by image processing combined with machine learning algorithms. In this study, the diseases in tomato leaves will be detected using new feature extraction method using conformable polynomials image features for accurate solution and faster detection of plant diseases through a machine learning model. The methodology of this study based on:•Preprocessing, feature extraction, dimension reduction and classification modules.•Conformable polynomials method is used to extract the texture features which is passed classifier.•The proposed texture feature is constructed by two parts the enhanced based term, and the texture detail part for textual analysis.•The tomato leaf samples from the plant village image dataset were used to gather the data for this model. The disease detected are 98.80 % accurate for tomato leaf images using SVM classifier. In addition to lowering financial loss, the suggested feature extraction method can help manage plant diseases effectively, improving crop yield and food security.

Application of Endophytic Bacteria from Tomato Stems to Control Bacterial Wilt Disease in Tomato and Enhance Plant Growth

Application of Endophytic Bacteria from Tomato Stems to Control Bacterial Wilt Disease in Tomato and Enhance Plant Growth

Unraveling the genetic basis of quantitative resistance to diseases in tomato: a meta-QTL analysis and mining of transcript profiles.

Whole-genome QTL mining and meta-analysis in tomato for resistance to bacterial and fungal diseases identified 73 meta-QTL regions with significantly refined/reduced confidence intervals. Tomato production is affected by a range of biotic stressors, causing yield losses and quality reductions. While sources of genetic resistance to many tomato diseases have been identified and characterized, stability of the resistance genes or quantitative trait loci (QTLs) across the resources has not been determined. Here, we examined 491 QTLs previously reported for resistance to tomato diseases in 40 independent studies and 54 unique mapping populations. We identified 29 meta-QTLs (MQTLs) for resistance to bacterial pathogens and 44 MQTLs for resistance to fungal pathogens, and were able to reduce the average confidence interval (CI) of the QTLs by 4.1-fold and 6.7-fold, respectively, compared to the average CI of the original QTLs. The corresponding physical length of the CIs of MQTLs ranged from 56kb to 6.37Mb, with a median of 921 kb, of which 27% had a CI lower than 500kb and 53% had a CI lower than 1Mb. Comparison of defense responses between tomato and Arabidopsis highlighted 73 orthologous genes in the MQTL regions, which were putatively determined to be involved in defense against bacterial and fungal diseases. Intriguingly, multiple genes were identified in some MQTL regions that are implicated in plant defense responses, including PR-P2, NDR1, PDF1.2, Pip1, SNI1, PTI5, NSL1, DND1, CAD1, SlACO, DAD1, SlPAL, Ph-3, EDS5/SID1, CHI-B/PR-3, Ph-5, ETR1, WRKY29, and WRKY25. Further, we identified a number of candidate resistance genes in the MQTL regions that can be useful for both marker/gene-assisted breeding as well as cloning and genetic transformation.

Role of Hydrogen Peroxide (H2O2) and Trichoderma koningii in Reducing Root Rot Disease of Tomato Causing by Fusarium solani

Two isolates of the pathogenic fungus Fusarium solani the causative of tomato root rot disease named Fs1 and Fs2 were isolated from tomato plants infected with root rot disease. Results showed that both examined isolates had significant effects on the percentage of seed germination of tomato and damping-off disease. Thus, isolate Fs1 was more effective than isolate Fs2, as it recorded a percentage of germination and damping-off 56.6 and 71.6% respectively, compared to the control treatment, which recorded 100 and 0% respectively. It was found that the use of hydrogen peroxide (H2O2) at concentrations of 50, 100 and 200 ppm had a significant effect on the fungal radial growth, dry weight (DW) and sporulation of F. solani. Antifungal activity of H2O2 appeared even at the lowest concentration (50 ppm), which inhibited radial growth to 49.33%, and decreased the dry weight to 498 mg, and the sporulation to 3.75×106 spores. Additionally, the results indicated significant inhibitory effects of hydrogen peroxide (H2O2) and the bioagent Trichoderma koningii on F. solani growth. It was noticed that H2O2 has compatible effects with T. koningii, where the antagonistic ability of T. koningii against F. solani increased when the concentration of H2O2 had increased. The results revealed that the treatment F. solani + H2O2 (200 ppm) + T. koningii significantly reduced the percentage of damping-off and plant survival to 9.40 and 5.39% respectively, in comparison with the control treatment and F. solani alone treatment, which reached 20.22, 14.48, 34.32 and 67.41% respectively.

Management of Tomato Bacterial Canker Disease by the Green Fabricated Silver Nanoparticles

Bacterial canker disease caused by Clavibacter michiganensis is a substantial threat to the cultivation of tomatoes, leading to considerable economic losses and global food insecurity. Infection is characterized by white raised lesions on leaves, stem, and fruits with yellow to tan patches between veins, and marginal necrosis. Several agrochemical substances have been reported in previous studies to manage this disease but these were not ecofriendly. Thus present study was designed to control the bacterial canker disease in tomato using green fabricated silver nanoparticles (AgNps). Nanosilver particles (AgNPs) were synthesized utilizing Moringa oleifera leaf extract as a reducing and stabilizing agent. Synthesized AgNPs were characterized using UV–visible spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray (EDX), and Fourier transform infrared spectrometry (FTIR). FTIR showed presence of bioactive compounds in green fabricated AgNPs and UV-visible spectroscopy confirmed the surface plasmon resonance (SPR) band in the range of 350 nm to 355 nm. SEM showed the rectangular segments fused together, and XRD confirmed the crystalline nature of the synthesized AgNPs. The presence of metallic silver ions was confirmed by an EDX detector. Different concentrations (10, 20, 30, and 40 ppm) of the green fabricated AgNPs were exogenously applied on tomato before applying an inoculum of Clavibacter michigensis to record the bacterial canker disease incidence at different day intervals. The optimal concentration of AgNPs was found to be 30 µg/mg that exhibited the most favorable impact on morphological (shoot length, root length, plant fresh and dry weights, root fresh and dry weights) and physiological parameters (chlorophyll contents, membrane stability index, and relative water content) as well as biochemical parameters (proline, total soluble sugar and catalase activity). These findings indicated a noteworthy reduction in biotic stress through the increase of both enzymatic and non-enzymatic activities by the green fabricated AgNPs. This study marks a first biocompatible approach in assessing the potential of green fabricated AgNPs in enhancing the well-being of tomato plants that affected with bacterial canker and establishing an effective management strategy against Clavibacter michiganensis. This is the first study suggests that low concentration of green fabricated nanosilvers (AgNPs) from leaf extract of Moringa oleifera against Clavibacter michiganensis is a promisingly efficient and eco-friendly alternative approach for management of bacterial canker disease in tomato crop.

Biologically inspired nanoformulations for the control of bacterial canker pathogens Clavibacter michiganensis subsp. michiganensis and subsp. capsici

Clavibacter michiganensis subsp. michiganensis (Cmm) and C. michiganensis subsp. capsici (Cmc) are phytopathogenic bacteria that cause bacterial canker disease in tomatoes and peppers, respectively. Bacterial canker disease poses serious challenges to solanaceous crops, causing significant yield losses and economic costs. Effective management necessitates the development of sustainable control strategies employing nanobiotechnology. In this study, the antibacterial effects of four Aspergillus sojae-mediated nanoformulations, including cobalt oxide nanoparticles (Co3O4 NPs), zinc oxide nanoparticles (ZnO NPs), cobalt ferrite nanoparticles (CoFe2O4 NPs), and CoFe2O4/functionalized multi-walled carbon nanotube (fMWCNT) bionanocomposite, were evaluated against Cmm and Cmc. The diameters of the zone of inhibition of A. sojae-mediated Co3O4 NPs, ZnO NPs, CoFe2O4 NPs, and CoFe2O4/fMWCNT bionanocomposite against Cmm and Cmc were 23.60 mm, 22.09 mm, 27.65 mm, 22.51 mm, and 19.33 mm, 17.66 mm, 21.64 mm, 18.77 mm, respectively. The broth microdilution assay was conducted to determine the minimal inhibitory and bactericidal concentrations. The MICs of Co3O4 NPs, ZnO NPs, CoFe2O4 NPs, and CoFe2O4/fMWCNT bionanocomposite against Cmm were 2.50 mg/mL, 1.25 mg/mL, 2.50 mg/mL, and 2.50 mg/mL, respectively. While, their respective MBCs against Cmm were 5.00 mg/mL, 2.50 mg/mL, 5.00 mg/mL, and 5.00 mg/mL. The respective MICs of Co3O4 NPs, ZnO NPs, CoFe2O4 NPs, and CoFe2O4/fMWCNT bionanocomposite against Cmc were 2.50 mg/mL, 1.25 mg/mL, 5.00 mg/mL, and 5.00 mg/mL. While, their respective MBCs against Cmc were 5.00 mg/mL, 2.50 mg/mL, 10.00 mg/mL, and 10.00 mg/mL. The morphological and ultrastructural changes of Cmm and Cmc cells were observed using field-emission scanning and transmission electron microscopy before and after treatment with sub-minimal inhibitory concentrations of the nanoformulations. Nanoformulation-treated bacterial cells became deformed and disrupted, displaying pits, deep cavities, and groove-like structures. The cell membrane detached from the bacterial cell wall, electron-dense particles accumulated in the cytoplasm, cellular components disintegrated, and the cells were lysed. Direct physical interactions between the prepared nanoformulations with Cmm and Cmc cells might be the major mechanism for their antibacterial potency. Further research is required for the in vivo application of the mycosynthesized nanoformulations as countermeasures to combat bacterial phytopathogens.

Volatile Organic Compounds of Wickerhamomyces anomalus Prevent Postharvest Black Spot Disease in Tomato.

Postharvest diseases, such as black spots caused by Alternaria alternata, have caused huge economic losses to the tomato industry and seriously restricted its development. In recent years, biological control has become a new method to control postharvest diseases of fruits and vegetables. Our research group screened W. anomalus, a yeast demonstrating a promising control effect on a postharvest black spot disease of tomatoes, and explored its physiological mechanism of prevention and control. Therefore, this study investigated the prevention and control effect of metabolites of W. anomalus on tomato black spot disease and the inhibition effect of main components on A. alternata. A GC-MS analysis found that isoamyl acetate was the main component of W. anomalus that played an inhibitory role. The results showed that isoamyl acetate could inhibit the growth of A. alternata and had a certain control effect on postharvest black spots in tomatoes. Our findings suggest that isoamyl acetate could be a promising alternative to fungicides for controlling postharvest black spots in tomatoes.

Multifactorial Tomato Leaf Disease Detection Based on Improved YOLOV5

Target detection algorithms can greatly improve the efficiency of tomato leaf disease detection and play an important technical role in intelligent tomato cultivation. However, there are some challenges in the detection process, such as the diversity of complex backgrounds and the loss of leaf symmetry due to leaf shadowing, and existing disease detection methods have some disadvantages in terms of deteriorating generalization ability and insufficient accuracy. Aiming at the above issues, a target detection model for tomato leaf disease based on deep learning with a global attention mechanism, TDGA, is proposed in this paper. The main idea of TDGA includes three aspects. Firstly, TDGA adds a global attention mechanism (GAM) after up-sampling and down-sampling, as well as in the SPPF module, to improve the feature extraction ability of the target object, effectively reducing the interference of invalid targets. Secondly, TDGA uses a switchable atrous convolution (SAConv) in the C3 module to improve the model’s ability to detect. Thirdly, TDGA adopts the efficient IoU loss (EIoU) instead of complete IoU loss (CIoU) to solve the ambiguous definition of aspect ratio and sample imbalance. In addition, the influences of different environmental factors such as single leaf, multiple leaves, and shadows on the performance of tomato disease detection are extensively experimented with and analyzed in this paper, which also verified the robustness of TDGA. The experimental results show that the average accuracy of TDGA reaches 91.40%, which is 2.93% higher than that of the original YOLOv5 network, which is higher than YOLOv5, YOLOv7, YOLOHC, YOLOv8, SSD, Faster R-CNN, RetinaNet and other target detection networks, so that TDGA can be utilized for the detection of tomato leaf disease more efficiently and accurately, even in complex environments.

A Fine-Grained Image Classification Method Built on MobileViT

With a rapid development of artificial intelligence technology, fine-grained image classification has gained widespread application. For mobile terminals, this paper introduces an image classification method built on MobileViT, and it can apply into fine-grained image classification. The original MobileViT model has been optimized in three ways. Initially, the h-swish activation function is used to enhance the network performance. Second, the cross-entropy loss function is used to further realize the parameter optimization and model accuracy improvement. Finally, a dropout layer is joined before the fully connected layer can effectively decrease the model recognition time and prevent over-fitting. Experimental data on public tomato disease datasets demonstrate that the improved fine-grained image classification method put forward in this paper exhibits higher classification accuracy, better stability and network generalization ability than other models.

Improved Tomato Disease Detection with YOLOv5 and YOLOv8

This study delves into the application of deep learning for precise tomato disease detection, focusing on four crucial categories: healthy, blossom end rot, splitting rotation, and sun-scaled rotation. The performance of two lightweight object detection models, namely YOLOv5l and YOLOv8l, was compared on a custom tomato disease dataset. Initially, both models were trained without data augmentation to establish a baseline. Subsequently, diverse data augmentation techniques were obtained from Roboflow to significantly expand and enrich the dataset content. These techniques aimed to enhance the models' robustness to variations in lighting, pose, and background conditions. Following data augmentation, the YOLOv5l and YOLOv8l models were re-trained and their performance across all disease categories was meticulously analyzed. After data augmentation, a significant improvement in accuracy was observed for both models, highlighting its effectiveness in bolstering the models' ability to accurately detect tomato diseases. YOLOv8l consistently achieved slightly higher accuracy compared to YOLOv5l, particularly when excluding background images from the evaluation.

Detection of bacterial spot disease on tomato leaves using a Convolutional Neural Network (CNN)

Tomato diseases have become a major concern to the tomato production sector around the world. A huge proportion of tomato crops deteriorate yearly during growth or after harvesting due to the infections caused by fungus, viruses and bacteria. Early detection of these diseases plays a crucial role in alleviating overall production loss. Over the past decades, farmers have been using visual observation to identify a crop disease in a field. However, the visual observation method is labour intensive, time consuming, and prone to human error. Currently, intelligent approaches have been widely used to detect and classify these diseases. The objective of this study is to design a convolutional neural network VGG16 net architecture that is able to detect tomato diseases on tomato leaves. A model that can successfully detect 5 tomato leaf diseases was developed: (1) Bacterial spot, (2) Early blight, (3) Late blight, (4) Septoria leaf and (5) Yellow leaf curl with a training accuracy of 0.9328.

A Fresh Look at Tomato Leaf Disease Recognition using Vision Transformers

Tomatoes is one of the major economically significant vegetables produced worldwide, contributing greatly to increased agricultural production and food security. However, tomato plants are unfortunately prone to a number of diseases, including several that target the leaves, which can significantly reduce crop productivity and quality. Recently, deep learning techniques have revolutionized the fields of computer vision and image analysis. By automatically learning hierarchical representations from raw pixel data. Transformer is a new deep learning technique that opens new possibilities for image understanding using self-attention mechanisms to capture global dependencies within input sequences. This approach is exemplified by the Vision Transformer (ViT). In this study, we utilize and evaluate the effectiveness of six variations of the Vision Transformer (ViT) architecture in the task of tomato leaf disease recognition. The variants include Mobile ViT, EANet, Swin ViT, ViT, Shift ViT, and Compact ViT. Utilizing a publicly available, multiple-source dataset of tomato leaf images containing various disease patterns. Performance for all models was evaluated and compared in classifying various types of tomato leaf diseases in terms of accuracy, loss, precision, recall, and F1-Score, and the results showed that. The CompactVit has achieved the best accuracy of 97% and precision of 97% and 96% for recall. While the mobile ViT has the lowest performance among all variations in tomato disease recognition, overall, ViT is showing its promise, and it can be utilized on a large scale for smart agriculture, which opens the door for further exploration of this area.

Monitoring of main tomato (Solánum lycopérsicum L.) diseases and methods of microbiological control of phytopathogens

The article highlights the main diseases of tomato plants caused by phytopathogenic microorganisms that cause great damage in many countries of the world, including Ukraine. It has been analyzed that phytopathogenic micromycetes that parasitize tomato plants cause huge losses in agricultural production, reducing the quality of products, and also release toxins that cause poisoning and numerous serious diseases that affect humans and animals that consume this product. It has been established that the most common tomato diseases caused by phytopathogenic fungi are late blight, alternaria, septoria, gray rot, spotted wilt virus, and bacterial tomato cancer. Microbiological means of control were analyzed as an alternative to chemical fungicides, which are considered safe methods in modern agriculture. They stimulate the defense mechanisms of plants, acting as inducers of plant resistance or as means of biological control. It was found that one of the common biological preparations in the fight against diseases and improving the growth of tomato culture are preparations containing Trichoderma spp. They are widely used as biofungicides against the pathogens Botrytis cinerea, Fusarium oxysporum, Rhizoctonia solani and Sclerotium rolfsii. The study of Mexican scientists on the testing of various isolates of the genus Trichoderma spp. Chinese scientists identified a strain of Pseudomonas aeruginosa (CQ-4) that can effectively control the spread of gray rot disease and identified bacteria of the genus Bacillus subtilis, which are characterized by notable antagonistic activity against a wide range of phytopathogenic bacteria and micromycetes and are a factor in biological control. Also, scientists from Egypt who conducted experiments on two endophytic fungi, namely Curvularia lunata and Nigrospora sphaerica, where their antagonistic action against phytopathogenic fungi and their ability to produce an important growth hormone and provide some necessary nutrients for plant growth were evaluated. A number of investigated antagonist strains are the basis or promising for the production of microbial preparations for controlling phytopathogens in agroecosystems and increasing the yield of tomato plants.

Exploring the genetic makeup of Xanthomonas species causing bacterial spot in Taiwan: evidence of population shift and local adaptation

The introduction of plant pathogens can quickly reshape disease dynamics in island agro-ecologies, representing a continuous challenge for local crop management strategies. Xanthomonas pathogens causing tomato bacterial spot were probably introduced in Taiwan several decades ago, creating a unique opportunity to study the genetic makeup and adaptive response of this alien population. We examined the phenotypic and genotypic identity of 669 pathogen entries collected across different regions of Taiwan in the last three decades. The analysis detected a major population shift, where X. euvesicatoria and X. vesicatoria races T1 and T2 were replaced by new races of X. perforans. After its introduction, race T4 quickly became dominant in all tomato-growing areas of the island. The genomic analysis of 317 global genomes indicates that the Xanthomonas population in Taiwan has a narrow genetic background, most likely resulting from a small number of colonization events. However, despite the apparent genetic uniformity, X. perforans race T4 shows multiple phenotypic responses in tomato lines. Additionally, an in-depth analysis of effector composition suggests diversification in response to local adaptation. These include unique mutations on avrXv3 which might allow the pathogen to overcome Xv3/Rx4 resistance gene. The findings underscore the dynamic evolution of a pathogen when introduced in a semi-isolated environment and provide insights into the potential management strategies for this important disease of tomato.