Diseased Tomato Plants Research Articles

Antibacterial, Herbicidal, and Plant Growth-Promoting Properties of Streptomyces sp. STD57 from the Rhizosphere of Adenophora stricta

Bacterial wilt triggered by the soil-borne pathogenic bacterium Ralstonia solanacearum is one of the most serious diseases in tomato plants, leading to huge economic losses worldwide. Biological control is considered an environmentally friendly and sustainable way to manage soil-borne diseases. In this study, Streptomyces sp. STD57 isolated from the rhizosphere of Adenophora stricta showed strong antibacterial activity against R. solanacearum. Pot experiments showed that strain STD57 exhibited a significant biocontrol effect (81.7%) on tomato bacterial wilt in the greenhouse environment. Furthermore, strain STD57 could inhibit the growth of weeds (Amaranthus retroflexus, Portulaca oleracea, and Echinochloa crusgalli) but promote the growth of crops (wheat, rice, and tomato). The plant growth-promoting substance was identified as indoleacetic acid (IAA) by high-pressure liquid chromatography–mass spectrometry and genome analysis. Coarse separation of the fermented extracts revealed that the antibacterial and herbicidal substances were mainly in the fermentation supernatant and belonged to different products. These findings suggested that strain STD57 may be a potential biocontrol and bioherbicide agent useful in agriculture.

Complete genome sequences of two novel Ralstonia jumbo phages isolated from leaf litter compost

Two Ralstonia phages, FLC1-1B and FLC4-3B, were isolated from leaf litter compost, using Ralstonia pseudosolanacearum, which is a causal agent of bacterial wilt disease, as a host. The genomic DNA sequences of FLC1-1B and FLC4-3B were determined and found to be 290,008 bp and 291,257 bp in length, respectively, and they were therefore classified as jumbo phages. However, they did not show high similarity to any jumbo phage genomic sequence in the NCBI nt database. The closest hit in a BLAST search was the jumbo phage ripduovirus RP12, with only 35% coverage and 77% sequence identity, whereas the FLC1-1B and FLC4-3B sequences were 99.0% identical. Based on these findings, FLC1-1B and FLC4-3B should be classified as members of a new genus in the order Caudoviricetes. FLC4-3B was found to suppress wilt disease in tomato plants, suggesting that it has potential as a biocontrol agent for managing R. pseudosolanacearum infections.

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.

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.

Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches

This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features into SHSIs, providing insights into the spectrum. This method enables the image analysis to perform assessments from SHSIs for health classification using Normalized Difference Vegetation Index (NDVI) values, which are meticulously compared with accurate hyperspectral data using metrics like mean absolute error (MAE) and root mean squared error (RMSE). This strategy enhances precision farming, environmental monitoring, and remote sensing accuracy. Results show that ResNet50’s architecture offers a robust framework for this study’s spectral and spatial analysis, making it a suitable choice over VGG16 and EfficientNet-B7 for transforming RGB images into SHSI. These simulated hyperspectral images offer a scalable and affordable approach for precise assessment of crop disease severity.

Exploring the Potential of Bacillus subtilis IS1 and B. amyloliquificiens IS6 to Manage Salinity Stress and Fusarium Wilt Disease in Tomato Plants by Induced Physiological Responses.

The intensified concerns related to agrochemicals' ecological and health risks have encouraged the exploration of microbial agents as eco-friendly alternatives. Some members of Bacillus spp. are potential plant-growth-promoting agents and benefit numerous crop plants globally. This study aimed to explore the beneficial effects of two Bacillus strains (B. subtilis strain IS1 and B. amyloliquificiens strain IS6) capable of alleviating the growth of tomato plants against salinity stress and Fusarium wilt disease. These strains were able to significantly promote the growth of tomato plants and biomass accumulation in pot trials in the absence of any stress. Under salinity stress conditions (150 mM NaCl), B. subtilis strain IS1 demonstrated superior performance and significantly increased shoot length (45.74%), root length (101.39%), fresh biomass (62.17%), and dry biomass (49.69%) contents compared to control plants. Similarly, B. subtilis strain IS1 (63.7%) and B. amyloliquificiens strain IS6 (32.1%) effectively suppressed Fusarium wilt disease and significantly increased plant growth indices compared to the pathogen control. Furthermore, these strains increased the production of chlorophyll, carotenoid, and total phenolic contents. They significantly affected the activities of enzymes involved in antioxidant machinery and the phenylpropanoid pathway. Hence, this study effectively demonstrates that these Bacillus strains can effectively alleviate the growth of tomato plants under multiple stress conditions and can be used to develop bio-based formulations for use in the fields.

Endophyte Bacillus vallismortis BL01 to Control Fungal and Bacterial Phytopathogens of Tomato (Solanum lycopersicum L.) Plants

Some strains of Bacillus vallismortis have been reported to be efficient biocontrol agents against tomato pathogens. The aim of our study was to assess the biocontrol ability of the endophytic strain BL01 Bacillus vallismortis through in vitro and field trials, as well as to verify its plant colonization ability and analyze the bacterial genome in order to find genes responsible for the biocontrol activity. We demonstrated in a gnotobiotic system and by confocal laser microscopy that the endophytic strain BL01 was able to colonize the endosphere and rhizosphere of tomato, winter wheat and oilseed rape. In vitro experiments demonstrated the inhibition activity of BL01 against a wide range of phytopathogenic fungi and bacteria. BL01 showed biological efficacy in two-year field experiments with tomato plants against black bacterial spotting by 40–70.8% and against late blight by 47.1% and increased tomato harvest by 24.9% or 10.9 tons per hectare compared to the control. Genome analysis revealed the presence of genes that are responsible for the synthesis of biologically active secondary metabolites, which could be responsible for the biocontrol action. Strain BL01 B. vallismortis can be considered an effective biocontrol agent to control both fungal and bacterial diseases in tomato plants.

Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network

This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing model performance. The proposed model incorporates a Closed-form Continuous-time Neural Network, a lightweight variant of liquid time-constant networks, and integrates Neural Circuit Policy to capture long-term dependencies in image patterns, reducing overfitting. Augmentation techniques such as random rotation and brightness adjustments were applied to the training data to improve generalization. The results demonstrate that the hybrid models outperform their single pre-trained CNN counterparts in both accuracy and computational cost, achieving a 97.15% accuracy on the test set with the proposed model, compared to around 94% for state-of-the-art pre-trained models. This study provides evidence of the effectiveness of hybrid CNN-RNN models in improving accuracy without increasing computational cost and highlights the potential of liquid neural networks in such applications.

Agriculture Robot Using Image Processing

Background: Agriculture is a cornerstone of the economy, but traditional farming is labor-intensive and time-consuming. Advances in technology, such as Convolutional Neural Networks (CNNs) and robotics, can help address challenges like disease detection, weed control, and labor shortages. Tomatoes, a key crop, benefit from proper management using these innovations. Objective: This project aims to design and develop an agriculture robot using IoT and Machine Learning to automate farming tasks, detect tomato plant diseases, and manage weeds more efficiently. Methods: The robot is controlled by an ESP32 microcontroller connected via Wi-Fi for real-time monitoring. The system uses CNNs for disease detection in tomato plants, along with a custom-trained dataset using the TensorFlow framework. Mechanical arms perform tasks like seeding, watering, spraying fertilizers, and harvesting. A user interface for disease detection was designed for real time disease detection. Results: The model achieved an accuracy of 95.0% for disease detection, 95.56% for fruit detection, and 95.14% for plant detection. These results highlight the effectiveness of the robot in managing tomato crops. Conclusion: The agriculture robot provides a comprehensive solution to key farming challenges, enhancing efficiency through automation and improving crop health monitoring. The integration of IoT and ML offers a pathway toward sustainable and profitable agriculture, particularly in managing tomatoes. Further improvements and scaling could expand its application to other crops and farming tasks. Keywords: Machine Learning, Internet of Things, Agricultural Robot, Disease Detection, Convolutional Neural Networks, Mechanical Tasks.

Development of a Web-based Tomato Plant Disease Detection and Diagnosis System using Transfer Learning Techniques

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

Suppressive Effect of Black Soldier Fly Larvae Frass on Fusarium Wilt Disease in Tomato Plants.

This study investigated the effect of black soldier fly larvae (BSFL) frass derived from BSFL reared on a diet composed of fruit/vegetable/bakery/brewery residues (FVBB diet) and on the Gainesville diet (GV diet) on the development of tomato (Solanum lycopersicum) Fusarium wilt caused by Fusarium oxysporum f. sp. lycopersici (FOL). Tomato plants were grown in a substrate inoculated with FOL that was amended (10%, v:v) or not (control) with either a commercial compost, pasteurized (70 °C for 1 h) frass from BSFL reared on a FVBB diet, non-pasteurized frass from BSFL reared on a FVBB diet, pasteurized frass from BSFL reared on the GV diet, or non-pasteurized frass from BSFL reared on the GV diet. The results show that frass from BSFL reared on the GV diet, irrespective of pasteurization, inhibited FOL root colonization and reduced the severity of tomato Fusarium wilt to a far greater extent than frass from BSFL reared on a FVBB diet and commercial compost made of peat, seaweed, and shrimps. This study suggests that BSFL frass, depending on the larval rearing diet, has the potential to serve as a pasteurized or non-pasteurized soil amendment with prophylactic properties against FOL in tomato plants, opening new avenues of research for the valorization of BSFL frass.

First report of bacterial wilt caused by Clavibacter michiganensis subsp. michiganensis affecting tomato in Iğdır

Tomato wilt disease caused by Clavibacter mihiganensis subsp. michiganensis (Cmm) is one of the most destructive tomato diseases and causes significant crop loss in both greenhouse and field tomato production areas worldwide. In this study, the presence of the causal agent of bacterial wilt disease in tomato plants was investigated in Aras Valley. Isolation was made from diseased plant samples and it was determined whether the strains were pathogenic by cellulase activity and HR test. The virulence, morphological and biochemical characteristics of the strains were determined. Strains that fatty acid methyl ester extraction, isolation and purification were performed were identified at species and subspecies level with % similarity index using gas chromatography system. The diagnosis was confirmed with the Biolog Gen III System and all strains were identified at the subspecies level with a % similarity index. As a result of this study, 57 strains were obtained in the isolation, and 39 of the strains were determined not to be pathogenic. Strain 18 was determined as the pathogen causing the most damage to tomato plants with 100 % disease severity. Strains were identified as Cmm at subspecies level with a similarity index of 71-87 % using gas chromatography system and 54-75 % similarity index with Biolog Gen III System. According to the heat map created, it was determined that the strains consisted of two main clusters. The presence of pathogen in Aras Valley was proved for the first time by this study.

Field isolates of Beauveria bassiana exhibit biological heterogeneity in multitrophic interactions of agricultural importance

Beauveria bassiana (Bb) is a widespread entomopathogenic fungus widely used in agriculture for crop protection. Other than pest control, fungi belonging to the B. bassiana complex represent an important microbial resource in agroecosystems, considering their multiple interactions with other microorganisms as antagonists of phytopathogens, or with plants as endophytic colonizers and growth promoters. Here, we characterised field collected or commercial isolates of B. bassiana relative to the environmental factors that affect their growth. We further compared the metabolome, the entomopathogenic potential and biocontrol activity of the tested isolates respectively on the insect pest Spodoptera littoralis or against the fungal plant pathogen Fusarium oxysporum. Our analysis revealed that the B. bassiana complex is characterised by a high level of inter-isolate heterogeneity in terms of nutritional requirements, establishment of intra- or inter-kingdom interactions, and the nature of metabolites produced. Interestingly, certain B. bassiana isolates demonstrated a preference for low nutrient plant-derived media, which hints at their adaptation towards an endophytic lifestyle over a saprophytic one. In addition, there was a noticeable variation among different B. bassiana isolates in their capacity to kill S. littoralis larvae in a contact infection test, but not in an intrahaemocoelic injection experiment, suggesting a unique level of adaptability specific to the host. On the other hand, most B. bassiana isolates exhibited similar biocontrol efficacy against the soil-dwelling ascomycete F. oxysporum f. sp. lycopersici, a pathogen responsible for vascular wilt disease in tomato plants, effectively averting wilting. Overall, we show that the effectiveness of B. bassiana isolates can greatly vary, emphasising the importance of isolate selection and nutritional adaptability consideration for their use in sustainable agriculture.

Use of Computer Vision Techniques for Recognition of Diseases and Pests in Tomato Plants

Use of Computer Vision Techniques for Recognition of Diseases and Pests in Tomato Plants

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.

Development of a Multiplex PCR Assay for the Detection of Tomato Wilt Caused by Coinfection of Fusarium brachygibbosum, Fusarium oxysporum, and Ralstonia solanacearum Based on Comparative Genomics.

Tomato is consumed worldwide as fresh or processed food products. However, soilborne diseases of tomato plants caused by coinfection of various pathogens result in great economic losses to the tomato industry. It is difficult to accurately identify and diagnose soilborne diseases of tomato plants caused by pathogen complexes. In this study, we investigated field diseases of tomato plants by pathogen isolation and molecular identification and found that tomato wilt was caused by coinfection of Fusarium brachygibbosum, F. oxysporum, and Ralstonia solanacearum. Therefore, developing a method for simultaneous detection of DNA from F. brachygibbosum, F. oxysporum, and R. solanacearum is of great importance to efficiently and accurately monitor disease development at different growth stages of tomato plants. In this study, we performed a comparative genomic analysis of F. brachygibbosum, F. oxysporum, and R. solanacearum and determined the primer sets for simultaneous detection of DNA from these target pathogens. Then, we tested the reagent and condition parameters of multiplex PCR, including primers, dNTP and Mg2+ concentrations, and annealing temperatures, to determine the optimal parameters of a multiplex PCR system. We evaluated the specificity, sensitivity, and stability of the multiplex PCR system based on the optimized reaction conditions. The multiplex PCR system can specifically identify 13 target pathogens from 57 different fungal and bacterial pathogens, at the lower detection limit of the three target pathogens at concentrations of 100 pg/μl. In addition, we can accurately identify the three pathogens in tomato plants using the optimized multiplex PCR method. These results demonstrated that the multiplex PCR method developed in this study can simultaneously detect DNA from F. brachygibbosum, F. oxysporum, and R. solanacearum in a single PCR system to accurately identify and diagnose the pathogen causing tomato wilt.

Viral complexity of tomatoes yellow mosaic and leaf curl diseases in lowland and highland areas

AbstractYellow mosaic and leaf curl diseases caused by whitefly‐transmitted begomo virus genera are important problems in tomato (Solanum lycopersicon L.) production in Indonesia, particularly on Java Island. The aims of this survey are to assess the percentages of disease incidence (DI) and disease severity index (DSI), evaluate the spread and complexity of begomovirus and crinivirus infections responsible for yellow mosaic and leaf curl diseases in tomato plants, and identify the species of whitefly insect vectors and their cryptic biotypes in tomato production centers located on Java Island, Indonesia. A total of 43 locations in East, Central, and West Java were surveyed during the dry season from July 2022 to September 2022. Disease incidence and severity were determined by observing 200 samples of tomato plants from five points at each location, with 40 plants per point of land. A total of 116 whitefly samples in microtubes and 215 symptomatic leaf samples were collected. The genus begomovirus and crinivirus infecting tomato were identified by PCR and RT‐PCR using genus‐specific primers. Similarly, whitefly species and their cryptic biotypes were identified by PCR using MCoI gene‐specific primers. Results showed that DI and DSI remarkably differed between the lowlands (66%–89%) and highlands (33%–62%) in the provinces of Central Java and West Java. Furthermore, PCR analysis of 215 infected tomato showing the symptoms of yellow mosaic and curly leaf revealed that three begomoviruses, one crinivirus and their mixed combination both begomoviruses, one crinivirus has been detected in all the samples. Similarly, the whitefly insect vector Bemisia tabaci biotype Q was identified in all areas, whereas Trialeurodes vaporariorum was found only in the East Java and Java highlands, respectively.

First Report of Enterobacter mori Causing Bacterial Wilt on Tomato in China.

Tomato (Solanum lycopersicum L.) is one of the most important vegetable crops in China. In October 2023, a new bacterial disease was discovered on tomato plants in a 0.3-acre farm's greenhouse (35.514806N, 118.996106E) in Longshan Town, Shandong Province, China. Over 50% of the tomato plants showed symptoms of stem rot, leaf wilt, or plant death. Three diseased tomato plants were collected for pathogen isolation and purification. Two leaf samples, each about 1 cm2, were cut from the junction area of healthy and diseased parts and disinfected with 75% alcohol for 60 s, followed by 0.1% HgCl2 for 90 s, and then washed three times with sterilized H2O. The samples were subsequently ground with 1.0 mL sterilized H2O. The ground samples were diluted to 10-4, 10-5, and 10-6 and then were plated on a potato dextrose agar (PDA) plate, respectively. White mucous bacterial colonies appeared at 28℃ for 24~48 h, no fungal colony was observed. Six bacterial colonies were randomly selected for gram staining and found to be gram-negative. To further determine their species classification, fragments of the 16SrDNA, hsp60, gyrB, and rpoB genes were separately amplified using previously reported PCR conditions and with primer pairs, including 27F/1492R (Wu et al., 2023), HSP60-F /HSP60-R (Gül et al., 2023), gyrB UP-1 / gyrB UP-2r (Yamamoto et al., 1995) and rpoB CM81-F / rpoB CM32b-R (Brady et al., 2008). Sequence analysis showed that the obtained sequences of the 16SrDNA, hsp60, gyrB, and rpoB genes among these six colonies were identical and 100%, 100%, 99.31%, and 99.36% similar to those of Enterobacter mori accessions OP601841 (with a coverage of 100%), MT199160 (83%), OP676246 (100%), and MN594495 (100%), at nucleotide level, respectively. Sequences of the above four genes of 23LSFQ were submitted to GenBank under the accession numbers PP461247, PP474090, PP136037, and PP136038, respectively. We selected one of these six colonies, 23LSFQ, for further analysis. The phylogenetic tree based on the concatenated sequences of the above four genes using the maximum likelihood method with MEGAX software showed that 23LSFQ is grouped with E. mori LMG25706 (NCBI: txid980518). To determine the pathogenicity of 23LSFQ , we sprayed 23LSFQ (OD600=0.8) onto five 30-day-old healthy plants of the tomato cultivars Alisa Craig, Jinpeng NO.1, and Chaobei, respectively. These seedlings were incubated in a chamber at 28°C with a 16 h light/ 8h dark photoperiod and 60% relative humidity. The leaves of the inoculated plants became curled and wilted at two days post inoculation (dpi) and appeared necrotic at 10 dpi. The symptoms were similar to those observed in field-infected tomato plants. No symptoms were observed on the plants inoculated with water. We further sequenced the re-isolated bacteria from the symptomatic inoculated seedlings. Results showed that they belong to E. mori. The experiment was repeated three times. E. mori has been found to cause diseases on peaches (Ahmad et al., 2021), watermelons (Wu et al., 2023), Canna indica, (Zhang et al., 2023), and strawberries (Ji et al., 2023). E. cloacae has been found to cause diseases on tomatoes in Heilongjiang province (Jin et al., 2023). This is the first report of E. mori causing leaf yellowing and wilting on tomatoes in China. These results are significant for the safe production and disease control of greenhouse tomatoes.

Defect Detection of Tomato Fruit Using Image Dataset

Abstract: The tomato crop is more susceptible to disease than any other vegetable, and it can be infected with over 200 diseases caused by different pathogens worldwide. Tomato plant diseases have become a challenge to food security globally. Currently, diagnosing and preventing tomato plant diseases is a challenge due to the lack of essential methods or tools.The traditional techniques of detecting plant disease are arduous and error-prone. Utilizing precise or automatic detection methods in spotting early plant disease can improve the quality of food production and reduce adverse effects. Deep learning has significantly increased the recognition accuracy of image classification and object detection systems in recent years. In this study, a 15-layer convolutional neural network is proposed as the backbone for single shot detector (SSD) to improve the detection of healthy, and three classes of tomato fruit diseases. The proposed model performance is compared with ResNet-50, AlexNet, VGG 16, and VGG19 as the backbone for Single shot detector. The findings of the experiment showed that the proposed CNN-SDD achieved 98.87% higher detection accuracy, which outperformed state-of-the-art models.

The study of iron- and copper-binding proteome of Fusarium oxysporum and its effector candidates.

Fusarium oxysporum f.sp. lycopersici is a phytopathogen which causes vascular wilt disease in tomato plants. The survival tactics of both pathogens and hosts depend on intricate interactions between host plants and pathogenic microbes. Iron-binding proteins (IBPs) and copper-binding proteins (CBPs) play a crucial role in these interactions by participating in enzyme reactions, virulence, metabolism, and transport processes. We employed high-throughput computational tools at the sequence and structural levels to investigate the IBPs and CBPs of F. oxysporum. A total of 124 IBPs and 37 CBPs were identified in the proteome of Fusarium. The ranking of amino acids based on their affinity for binding with iron is Glu > His> Asp > Asn > Cys, and for copper is His > Asp > Cys respectively. The functional annotation, determination of subcellular localization, and Gene Ontology analysis of these putative IBPs and CBPs have unveiled their potential involvement in a diverse array of cellular and biological processes. Three iron-binding glycosyl hydrolase family proteins, along with four CBPs with carbohydrate-binding domains, have been identified as potential effector candidates. These proteins are distinct from the host Solanum lycopersicum proteome. Moreover, they are known to be located extracellularly and function as enzymes that degrade the host cell wall during pathogen-host interactions. The insights gained from this report on the role of metal ions in plant-pathogen interactions can help develop a better understanding of their fundamental biology and control vascular wilt disease in tomato plants.