Disease Of Tomato Research Articles

Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model

Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model

Comparative study of endophytic bacterial strains from non-host crops for enhancing plant growth and managing early blight in tomato

Bacillus pseudomycoides, Paenibacillus polymyxa, and B. velezensis are potent bacterial endophytes, which typically exhibit host-specific interactions. However, comparative studies of these endophytes in vitro and in planta in non-host crops are lacking. Therefore, in this study, we evaluated the potential of endophytes B. pseudomycoides strain HP3d, P. polymyxa strain PGSS1, B. velezensis strain A6, and P42, isolated from various crop ecosystems in promoting plant growth and inducing systemic resistance against early blight disease in tomato. In vitro, endophytes exhibited 44.44–55.56% and 37.50–87.50% inhibition of Alternaria solani in dual culture and volatilome bioassay, respectively. In the glasshouse, individual and combined applications via seed treatment (ST), seedling dip (SD), and foliar spray (FS) significantly enhanced shoot growth (23.63–57.61%), root growth (43.27–118.23%), number of leaves (77.52–93.58%), number of shoots (33.42–45.28%) and root dry matter (42.17–43.86%), reducing early blight (PDI) by 70.95–76.12% compared to uninoculated control. Enzymatic activities, including such as polyphenol oxidase (30–40 fold), peroxidase (65.00–75.00 fold), superoxide dismutase (34.20–37.20 fold) and phenylalanine ammonia-lyase (44.44–45.56 fold) were elevated post-inoculation in endophytes treated tomato plants challenged with A. solani compared to control treated only with A. solani and declined after the fifth day. The total chlorophyll content declined from the 0th to the 10th day, but endophyte treated plants exhibited lesser reductions (2.03–2.09) than uninoculated control. Field trials confirmed the glasshouse findings, showing reduced early blight and improved growth parameters in tomato where the ST + SD + FS combination emerged as the most effective treatment for all endophytes showing 1.06–1.88 fold increase in fruit yield per plant and 28.92–32.52% decrease in PDI compared to untreated control. Thus, the study highlights the broad-spectrum potential of these strains in promoting plant growth and controlling early blight in tomato, demonstrating non-host specificity. These endophytes offer eco-friendly alternatives to chemical pesticides, supporting sustainable agriculture. Their success in field trials suggests the potential for commercialization and large-scale use across diverse crops and pave the way for further interdisciplinary research to optimize their application in integrated pest management strategies.

Automatic visual recognition for leaf disease based on enhanced attention mechanism

Recognition methods have made significant strides across various domains, such as image classification, automatic segmentation, and autonomous driving. Efficient identification of leaf diseases through visual recognition is critical for mitigating economic losses. However, recognizing leaf diseases is challenging due to complex backgrounds and environmental factors. These challenges often result in confusion between lesions and backgrounds, limiting information extraction from small lesion targets. To tackle these challenges, this article proposes a visual leaf disease identification method based on an enhanced attention mechanism. By integrating multi-head attention mechanisms, this method accurately identifies small targets of tomato lesions and demonstrates robustness in complex conditions, such as varying illumination. Additionally, the method incorporates Focaler-SIoU to enhance learning capabilities for challenging classification samples. Experimental results showcase that the proposed algorithm enhances average detection accuracy by 10.3% compared to the baseline model, while maintaining a balanced identification speed. This method facilitates rapid and precise identification of tomato diseases, offering a valuable tool for disease prevention and economic loss reduction.

Research on tomato disease image recognition method based on DeiT

Tomatoes, globally cultivated and economically significant, play an essential role in both commerce and diet. However, the frequent occurrence of diseases severely affects both yield and quality, posing substantial challenges to agricultural production worldwide. In China, where tomato cultivation is carried out on a large scale, disease prevention and identification are increasingly critical for enhancing yield, ensuring food safety, and advancing sustainable agricultural practices. As agricultural production scales and the demand for efficient methodologies grows, traditional disease recognition methods no longer meet current needs. The agricultural sector's move towards more modern and scalable production methods necessitates more effective and precise disease recognition technologies to support swift decision-making and timely preventive actions. To address these challenges, this paper proposes a novel tomato disease recognition method that integrates the data-efficient image transformers (DeiT) model with strategies like exponential moving average (EMA) and self-distillation, named EMA-DeiT. By leveraging deep learning technologies, this method significantly improves the accuracy of disease recognition. The enhanced EMA-DeiT model demonstrated exemplary performance, achieving a 99.6 % accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2 % on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1 % accuracy on the PlantDoc dataset and 97.6 % on the Tomato-Village dataset. Utilizing the improved DeiT model, a comprehensive tomato disease recognition system was developed, featuring modules for image collection, disease detection, and information display. This system facilitates an integrated process from image collection to intelligent disease analysis, enabling agricultural workers to promptly understand and respond to disease occurrences. This system holds significant practical value for implementing precision agriculture and enhancing the efficiency of agricultural production.

Multiple Acquisitions of XopJ2 Effectors in Populations of Xanthomonas perforans.

Type III effectors (T3Es) are major determinants of Xanthomonas virulence and targets for resistance breeding. XopJ2 (synonym AvrBsT) is a highly conserved YopJ-family T3E acquired by X. perforans, the pathogen responsible for bacterial spot disease of tomato. In this study, we characterized a new variant (XopJ2b) of XopJ2, which is predicted to have a similar three-dimensional (3D) structure as the canonical XopJ2 (XopJ2a) despite sharing only 70% sequence identity. XopJ2b carries an acetyltransferase domain and the critical residues required for its activity, and the positions of these residues are predicted to be conserved in the 3D structure of the proteins. We demonstrated that XopJ2b is a functional T3E and triggers a hypersensitive response (HR) when translocated into pepper cells. Like XopJ2a, XopJ2b triggers HR in Arabidopsis that is suppressed by the deacetylase, SOBER1. We found xopJ2b in genome sequences of X. euvesicatoria, X. citri, X. guizotiae, and X. vasicola strains, suggesting widespread horizontal transfer. In X. perforans, xopJ2b was present in strains collected in North America, Africa, Asia, Australia, and Europe, whereas xopJ2a had a narrower geographic distribution. This study expands the Xanthomonas T3E repertoire, demonstrates functional conservation in T3E evolution, and further supports the importance of XopJ2 in X. perforans fitness on tomato. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Early plant disease detection by Raman spectroscopy: An open-source software designed for the automation of preprocessing and analysis of spectral dataset

This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (S. lycopersicum) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by Clavibacter michiganesis subsp. michiganensis (Cmm), and the tomato vein-greening associated with Candidatus Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by Bactericera cockerelli insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.

Extracellular DNA as a Strategy to Manage Vascular Wilt Caused by Fusarium oxysporum in Tomato (Solanum lycopersicum L.) Based on Its Action as a Damage-Associated Molecular Pattern (DAMP) or Pathogen-Associated Molecular Pattern (PAMP)

Vascular wilt is an important tomato disease that affects culture yields worldwide, with Fusarium oxysporum (F.o) being the causal agent of this infection. Several management strategies have lost effectiveness due to the ability of this pathogen to persist in soil and its progress in vascular tissues. However, nowadays, research has focused on understanding the plant defense mechanisms to cope with plant diseases. One recent and promising approach is the use of extracellular DNA (eDNA) based on the ability of plants to detect their self-eDNA as damage-associated molecular patterns (DAMPs) and pathogens’ (non-self) eDNA as pathogen-associated molecular patterns (PAMPs). The aim of this work was to evaluate the effect of the eDNA of F.o (as a DAMP for the fungus and a PAMP for tomato plants) applied on soil, and of tomato’s eDNA (as a DAMP of tomato plants) sprayed onto tomato plants, to cope with the disease. Our results suggested that applications of the eDNA of F.o (500 ng/µL) as a DAMP for this pathogen in soil offered an alternative for the management of the disease, displaying significantly lower disease severity levels in tomato, increasing the content of some phenylpropanoids, and positively regulating the expression of some defense genes. Thus, the eDNA of F.o applied in soil was shown to be an interesting strategy to be further evaluated as a new element within the integrated management of vascular wilt in tomato.

Antifungal activity of citral against Fusarium wilt in tomatoes and induction of the upregulation of glucanase, chitinase, and thaumatin-like protein plant defense genes

Fusarium wilt of tomatoes, caused by the soil-borne fungus Fusarium oxysporum f. sp lycopersici (FOL), is one of the devastating diseases of tomatoes, causing substantial economic losses worldwide. It is primarily controlled with chemical fungicides. The present study assessed the antifungal activity of citral against FOL under greenhouse conditions and investigated its effects on three plant defense genes: glucanase, chitinase, and thaumatin-like protein (TLP) using Real-time- quantitative PCR (RT-qPCR). Greenhouse experiments demonstrated that citral oil at 1024 and 512 µg/mL significantly reduced wilt severity from 61.7 % to 28.3 and 33.3 %, respectively. RT-qPCR results revealed that citral upregulates the expression of the three defense genes to varying extents, highlighting their role in resistance induction in tomatoes against FOL. Chitinase showed the highest differential expression with a 9.54-fold increase at 12 hours post-treatment (hpt), followed by TLP with a 6.96-fold increase at 12hpt, and glucanase with a 3.68-fold increase at 24hpt. These findings suggest citral is a promising biocontrol agent against fusarium wilt in tomatoes. However, field trials are recommended to validate these results under open field conditions.

Potential of Small Molecules Piperidine and Pyrrolidine Against Copper-Resistant Xanthomonas perforans, Causal Agent of Bacterial Spot of Tomato.

Bacterial spot of tomato (BST), caused by Xanthomonas perforans, is an economically important disease of tomatoes in Florida. Due to the heavy reliance on copper-based bactericides for control of BST, copper-resistant strains of X. perforans are widely distributed in Florida, leading to reduced efficacy of copper-based bactericides for disease control. There is a need for alternative chemical control strategies to effectively manage this disease in tomato production. In this study, two small molecules, piperidine and pyrrolidine, were evaluated for their efficacy against the copper-resistant X. perforans strain GEV 485 in laboratory, greenhouse, and field experiments. In in vitro experiments, piperidine and pyrrolidine at concentrations as low as 2 mg/L and 16 mg/L, respectively, significantly (P<0.001) reduced bacterial populations within 4 h of incubation compared to the untreated control, while Kocide 3000, the grower copper-based bactericide standard, at 0.9 g/L and 2.1 g/L (full label rate) did not significantly reduce bacterial populations. When tested as foliar sprays in the greenhouse, pyrrolidine at 128 mg/L significantly (P <0.001) reduced disease severity compared to the untreated control, with an equivalent efficacy to Kocide 3000 (copper hydroxide at 2.1 g/L). Kocide 3000 at 1.0 g/L, in combination with piperidine at 64 mg/L and pyrrolidine at 64 and 128 mg/L significantly improved the efficacy in disease control compared to untreated controls and Kocide 3000 at 1.0 g/L alone. In field trials, both small molecules demonstrated equivalent or superior efficacy to ManKocide (copper hydroxide + mancozeb) against X. perforans compared to the untreated control. This study demonstrated for the first time the potential of piperidine and pyrrolidine for controlling bacterial spot of tomato.

Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets

Recent advances in deep neural networks in terms of convolutional neural networks (CNNs) have enabled researchers to significantly improve the accuracy and speed of object recognition systems and their application to plant disease and pest detection and diagnosis. This paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios. The review shows that only a few studies available in the literature used data from real agricultural fields such as the PlantDoc dataset. The paper also reveals overoptimistic results of the huge number of studies in the literature that used the PlantVillage dataset collected under (controlled) laboratory conditions. This finding is consistent with the characteristics of the dataset, which consists of leaf images with a uniform background. The uniformity of the background images facilitates object detection and classification, resulting in higher performance-metric values for the models. However, such models are not very useful in agricultural practice, and it remains desirable to establish large datasets of plant diseases under real conditions. With some of the self-generated datasets from real agricultural fields reviewed in this paper, high performance values above 90% can be achieved by applying different (improved) CNN architectures such as Faster R-CNN and YOLO.

Solanum pimpinellifolium exhibits complex genetic resistance to Pseudomonas syringae pv. tomato.

Pseudomonas syringae pv. tomato (Pst) is the causal agent of bacterial speck disease in tomatoes. The Pto/Prf gene cluster from Solanum pimpinellifolium was introgressed into several modern tomato cultivars and provided protection against Pst race 0 strains for many decades. However, virulent Pst race 1 strains that evade Pto-mediated immunity now predominate in tomato-growing regions worldwide. Here we report the identification of resistance to a Pst race 1 strain (Pst19) in the wild tomato accession S. pimpinellifolium LA1589 (hereafter LA1589), using our rapid high-throughput seedling screen. LA1589 supports less bacterial growth than cultivars, and does not exhibit a hypersensitive response to Pst19. We tested an existing set of 87 Inbred Backcross Lines (IBLs) derived from a cross between susceptible Solanum lycopersicum E-6203 and Solanum pimpinellifolium LA1589 for resistance to Pst19. Using single-marker analysis, we identified three genomic regions associated with resistance. Bacterial growth assays on IBLs confirmed that these regions contribute to resistance in planta. We also mapped candidate genes associated with resistance in a cross between the Solanum lycopersicum var. lycopersicum cultivar Heinz BG-1706 and S. pimpinellifolium LA1589. By comparing candidates from the two mapping approaches, we were able to identify 3 QTL and 5 candidate genes in LA1589 for a role in resistance to Pst19. This work will assist in molecular marker-assisted breeding to protect tomato from bacterial speck disease.

New Method for Tomato Disease Detection Based on Image Segmentation and Cycle-GAN Enhancement

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model’s accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model’s ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

MobileNet-CAL: Tomato pest and disease classification method based on transfer learning and attention mechanism

Based on the MobileNetV3 model, this paper introduces the Coordinate attention (CA) attention mechanism and transfer learning to propose the MobileNet-CAL model to solve the problems of insufficient attention to model channel information, long classification training time, high training cost, and weak generalization ability. The model combines data enhancement, attention mechanism and transfer learning technology to improve the classification effect of the model, and updates the fully connected layer at the same time. Finally, the model is optimized by combining the Auxiliary loss function and the ASGD optimizer to improve the classification accuracy of the model, speed up the collection speed, and enhance the robustness of the model. Finally, the model is evaluated from multiple angles such as Accuracy, F1-score, Precision, Recall, and Specificity in the tomato pest and disease leaf dataset. Its Accuracy reaches 98.59% an accuracy of, which is improved by, and by, 4.47%respectively, compared with the original model. Experiments show that MobileNet-CAL can improve the classification accuracy to a certain extent, verifying that the model proposed 4.05%、3.43%、16.57% in 4.13%this paper is effective in the classification task of tomato pest and disease leaves.

Immunomodulating melatonin-decorated silica nanoparticles suppress bacterial wilt (Ralstonia solanacearum) in tomato (Solanum lycopersicum L.) through fine-tuning of oxidative signaling and rhizosphere bacterial community

BackgroundTomato (Solanum lycopersicum L.) production is severely threatened by bacterial wilt, caused by the phytopathogenic bacterium Ralstonia solanacearum. Recently, nano-enabled strategies have shown tremendous potential in crop disease management.ObjectivesThis study investigates the efficacy of biogenic nanoformulations (BNFs), comprising biogenic silica nanoparticles (SiNPs) and melatonin (MT), in controlling bacterial wilt in tomato.MethodsSiNPs were synthesized using Zizania latifolia leaves extract. Further, MT containing BNFs were synthesized through the one-pot approach. Nanomaterials were characterized using standard characterization techniques. Greenhouse disease assays were conducted to assess the impact of SiNPs and BNFs on tomato plant immunity and resistance to bacterial wilt.ResultsThe SiNPs and BNFs exhibited a spherical morphology, with particle sizes ranging from 13.02 nm to 22.33 nm for the SiNPs and 17.63 nm to 21.79 nm for the BNFs, indicating a relatively uniform size distribution and consistent shape across both materials. Greenhouse experiments revealed that soil application of BNFs outperformed SiNPs, significantly enhancing plant immunity and reducing bacterial wilt incidence by 78.29% in tomato plants by maintaining oxidative stress homeostasis via increasing the activities of antioxidant enzymes such as superoxide dismutase (31.81%), peroxidase (32.9%), catalase (32.65%), and ascorbate peroxidase (47.37%) compared to untreated infected plants. Additionally, BNFs induced disease resistance by enhancing the production of salicylic acid and activating defense-related genes (e.g., SlPAL1, SlICS1, SlNPR1, SlEDS, SlPD4, and SlSARD1) involved in phytohormones signaling in infected tomato plants. High-throughput 16 S rRNA sequencing revealed that BNFs promoted growth of beneficial rhizosphere bacteria (Gemmatimonadaceae, Ramlibacter, Microscillaceae, Anaerolineaceae, Chloroplast and Phormidium) in both healthy and diseased plants, while suppressing R. solanacearum abundance in infected plants.ConclusionOverall, these findings suggest that BNFs offer a more promising and sustainable approach for managing bacterial wilt disease in tomato plants.Graphical

Intelligent agricultural robotic detection system for greenhouse tomato leaf diseases using soft computing techniques and deep learning

The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.

Reverse-transcription recombinase-aided amplification and CRISPR/Cas12a-based universal detection for fast screening and accurate identification of six pospiviroids infecting Solanaceae crops.

Pospiviroids, members of the genus Pospiviroid, can cause severe diseases in tomato and other Solanaceae crops, causing considerable economic losses worldwide. Six pospiviroids including potato spindle tuber viroid (PSTVd), tomato chlorotic dwarf viroid (TCDVd), tomato planta macho viroid (TPMVd), Columnea latent viroid (CLVd), pepper chat fruit viroid (PCFVd), and tomato apical stunt viroid (TASVd) are regulated in many countries and organizations. Rapid, accurate detection is thus crucial for controlling the spread of these pospiviroids. For simultaneous detection of these six pospiviroids, we developed a rapid, visual method that uses a reverse transcription recombinase-aided amplification (RT-RAA) assay coupled with a clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 12a (CRISPR/Cas12a) system. In particular, this technique could achieve both universal detection and specific identification of the six target pospiviroids within 40 min. The universal detection could diagnose the six target pospiviroids in a single reaction, and the specific identification could identify each target pospiviroid without cross-reactivity of other pospiviroids. The sensitivity limits for the target pospiviroids detection with the proposed detection method were higher than those of the conventional reverse transcription-polymerase chain reaction (RT-PCR) method. We designed an RT-RAA-CRISPR/Cas12a-based universal detection method for both large-scale screening and accurate identification of the six target pospiviroids, which is appropriate for on-site detection. Our study results can aid in performing rapid, large-scale screening of multiple pests simultaneously. © 2024 Society of Chemical Industry.

Bioactivity of a dihydroxy flavone produced by Penicillium EU0013 against the wilt pathogen of tomato

Bioassay-guided isolation of a dihydroxy flavone from the cell extract of Penicillium EU0013 was carried out using preparative LCMS and the structure was interpreted using 1D and 2D NMR spectra. The fungus was fermented in three different nutrient media i.e. Glucose Nutrient Broth, Glucose Peptone Yeast Broth, and Yeast Extract Broth and the cell extract was obtained using organics. Comparative LCMS profiles of the fungus confirmed the enhanced production of dihydroxy flavone in GPYB media. In this study, the pathogenicity of the dihydroxy flavone is reported for the first time against Fusarium oxysporum f.sp. lycopersici, wilt causing pathogen of a tomato plant. In MIC experiments, the compound inhibited the pathogen at 80 µg mL−1. Molecular docking studies of the compound with Fol tomatinase showed five binding interactions. These docking studies may help explore dihydroxy flavones as a lead for developing new fungicides to prevent wilt disease in tomatoes.

In vitro bioefficacy of biocontrol agents against Fusarium oxysporum f.sp. lycopersici, causing wilt disease of tomato

In vitro bioefficacy of biocontrol agents against Fusarium oxysporum f.sp. lycopersici, causing wilt disease of tomato

Mob-psp: modified MobileNet-V2 network for real-time detection of tomato diseases

Mob-psp: modified MobileNet-V2 network for real-time detection of tomato diseases

A search for tomato disease resistance genes using molecular markers to create new genotypes

Background. The creation of tomato cultivars and hybrids with a complex of resistance genes is the main task of a breeder. This process can be accelerated through the use of molecular markers at the stages of initial forms selection and the offspring analysis. There is a large amount of information in the literature about DNA markers of resistance genes. Their significant part was recommended for the use in marker-assisted breeding. The purpose of our work was to screen a collection of tomato varieties and hybrids using molecular markers of genes for resistance to the most common diseases of open-ground tomato (late blight, root nematodes, tomato bronzing virus (TSWV)) and to identify gene sources for breeding work. The following markers were selected for research: Sw-5-2 (Sw-5b gene of resistance to TSWV), Mi23 (Mi1.2 gene of resistance to the root-knot nematode), 2 markers NC-LB-9-78 and NC-LB-9-79 (late blight resistance gene Ph-3). During the work, a collection of 46 accessions of tomato cultivars and hybrids was analyzed. Results. The molecular genetic analysis has yielded clear, reproducible fragments that corresponded to the expected ones. All the used markers were codominant. Analysis of the studied collection accessions found the analyzed genes to be polymorphic. The cultivars and hybrids F1 of tomato identified as promising for the use in breeding have resistance to root nematodes (F1 hybrids: A-01, ‘Imitator’, ‘Manon’, cultivars ‘Elegiya’ and ‘Buoy-Tur’), to tomato spotted wilt virus (TSWV) (F1 hybrids: A-01, ‘Manon’ and cultivar ‘Buoy-Tur’), and also to late blight (hybrids F1: А-01, ‘Azhur’, ‘Barin’, ‘Vlastelin stepej’, ‘Zhirdyaj’, Luchshij SeDeK, ‘Manon’ and cultivars: ‘Buoy-Tur’, ‘Zefir v shokolade’, ‘Zolotaya kaplya’, ‘Krasavec’, ‘Lodochka’, ‘Metelitsa’, ‘Alice's Dream’, ‘Sibirskij tigr’, ‘Slavyanskij shedevr’, ‘Elegiya’). It is advisable to use these accessions as sources of resistance genes. Based on the obtained data, five initial forms were selected for breeding and assessed for the intracultivar polymorphism of the studied genes. Cultivars ‘Krasavec’ and ‘Sibirskij tigr’ were used as parent forms. Their hybridization was carried out and hybrid forms homozygous for the dominant allele of the Ph-3 gene were obtained. Conclusions. The use of molecular markers in the conducted study allowed screening the collection of tomato varieties and hybrids for the presence of resistance genes to the most common diseases. Based on the obtained data, parental pairs were selected, hybridization carried out, and hybrid forms with the late blight resistance gene obtained.