Disease Of Tomato Research Articles

First report of tomato wilt caused by Fusarium brachygibbosum in China.

Tomato (Solanum lycopersicum) is a common vegetable in the Solanaceae family, which as a nutritious vegetable, is widely planted in China. In July 2022, typical wilt symptoms were observed in tomato fields located in the regions of Shiyan (31.343781°N, 110.901005°E), Hubei. Surveys were performed on tomato plants showing symptoms of leaf chlorosis, dry wilt, and vascular wilts on stem and root. The disease incidence ranged from 40% to 70% in 12 surveyed fields, 11.2 ha in total.. After using a sterilized scalpel to cut a small piece of diseased tomato stem and root tissue, diseased tissue was surface disinfested in 75% ethanol for 30s, placed onto potato dextrose agar (PDA) medium,and incubated at 25℃ for 3 days. Then, the emerging single fungal hypha tip was cut and transferred into PDA plates to obtain single spore isolates. Sixteen fungi growing on PDA plates were initially white colonies with abundant aerial mycelium. After seven days of growth, the center of the plate was yellow to orange and red pigmentation was produced. The five-day-old cultures grown on mung bean medium produced macroconidia that were scarce and scattered, three to four septa, wide central cells, slightly sharp apexes, and ranged in size from 12.6-23.6 μm×2.8-4.1 μm (n=30). Microconidia were slightly curved, ovoid with zero to two septa, measuring 5.2-11.8 μm×1.8-2.7μm (n=30). Spherical chlamydospores were terminal or intercalary, and measured a diameter of 8.1-11.6 μm (n =30). Therefore, sixteen isolates were morphologically identified as Fusarium spp. Moreover, the genomic DNA of the isolates (HBSY-1,HBSY-2, and HBSY-3) was extracted for amplification and sequencing of the regions of internal transcribed spacer (ITS) (White et al., 1990), nuclear large subunit rRNA (nLSU) (O'Donnell, 1992; Vilgalys and Hester, 1990), and the translation elongation factor 1-alpha (EF1-α) (O'Donnell et al. 1998) with primers ITS1/ITS4, NL1/LR3, and EF1/2, respectively. Sequences were submitted to GenBank under accession numbers OP959509, OQ568650, OQ568651 (ITS), OQ186731, OQ568652, OQ568653 (nLSU), and OP957576, OQ572485, OQ572486 (EF1-α). BLASTn showed that the sequences ITS, nLSU, and EF1-α were matched 99.61% (508/510 bp; KU528864.1), 99.90% (993/994 bp; GQ505450.1), and 99.85% (651/652; ON032449.1) to Fusarium brachygibbosum, respectively. Multilocus phylogenetic analysis showed that the isolate was of the same clade as F. brachygibbosum. Therefore, morphological characterization and molecular data identified the fungus as F. brachygibbosum. Pathogenicity of the isolate (HBSY-1) was tested on ten seedlings of tomato (cv. Hezuo908). Tomatoes were inoculated by spraying with conidial suspensions (1×107 spores/mL) at the rootstock region of each plant. In addition, ten negative control plants were treated with sterile water. All plants were incubated in an artificial climate box (LongYue, ShangHai) at 25℃ for 12 days. The experiment was repeated three times. Twelve days later, inoculated tomatoes developed typical wilting symptoms of leaves and vascular wilts of stem and root, while the control plants remained healthy. Thus, pathogens were reisolated from the stems of inoculated plants and not from control plants. To our knowledge, this is the first report of F. brachygibbosum causing leaf wilt and vascular wilts of stem and root on tomatoes in China.

Penerapan Metode Segmentasi Gabor Filter Dan Algoritma Support Vector Machine Untuk Pendeteksian Penyakit Daun Tomat

This research discusses about processing a formulation that we can give to diseased tomato leaves. Gabor Filter is a method used to detect textures using frequency and orientation parameters. The Support Vector Machine (SVM) algorithm is an algorithm that can be used classifying tomato leaf diseases. The purpose of this research is to determine the accuracy of the Gabor Filter segmentation and the Support Vector Machine Algorithm for detecting tomato leaf disease to facilitate farmers in analyzing diseases on tomato leaves. The input will go through pre-processing of RGB pixels to Greyscale ones before being processed using Gabor Filter. This Gabor Filter process segments the image to produce a magnitude value. The results of the image magnitude values here will be seen and will enter the classification process using SVM. The SVM algorithm aims to find the best hyperlane on tomato leaves that have been segmented to separate classes in the input space. The application of the SVM method with class classification of tomato leaves by calculating the energy value and entropy of the extraction results, assisted by 12 features, namely: CiriR, Feature G, FeatureB, Standard DeviationR, Standard DeviationG, Standard DeviationB, SkewnessR, SkewnessG, SkewnessB, Mean, Energy, Entropy are used to the simplity classification process with a high degree of accuracy. The process of classification of tomato leaf disease with test data of 600 images managed to get an accuracy value of 74.1667%. In order to facilitate the performance of farmers in predicting tomato leaf disease.

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop.

Response of bacterial biovars to inheritance pattern of bacterial wilt disease in tomato

ABSTRACT Knowledge of the genetic control of disease tolerance is necessary for effective transfer of desirable genes in resultant progenies leading to development of disease tolerant cultivars and hybrids. Bacterial wilt (BW) disease-resistant varieties of tomato (Solanum lycopersicum L.) express differential reactions to bacterial biovars distributed in specific regions. Genetic control of host tolerance to BW disease was studied using the generations P1, P2, F1, F2, BC1, BC2 of three tolerant × susceptible crosses (“Utkal Kumari × CLN- 2460E;” “Utkal Kumari × CLN-2764A;” “Utkal Deepti × CLN-2460E”) involving two tolerant (“Utkal Kumari,” “Utkal Deepti”), and two susceptible (“CLN-2460E” and “CLN-2764A”) genotypes exposed to the virulent bacterial biovars III and VI. The inheritance pattern indicated tolerance to BW disease was conditioned by a single dominant gene in three crosses when exposed to both biovars. Genetic control of bacterial wilt resistance did not depend on the interaction between genotype and bacterial biovars tested. Predominance of non-additive gene action and duplicate epistasis for conditioning of first appearance and incidence of BW disease in crosses indicated hindrance in progress through conventional selection. Selection for improvement of BW disease-related traits should be delayed to later segregating generations. The modified bulk method of selection appeared to be best for improving BW disease-related traits of tomato. Tomato hybrids tolerant to BW disease could be developed with the involvement of one parent tolerant to this disease.

Tomato pests and diseases in Bangladesh and India: farmers’ management and potential economic gains from insect resistant varieties and integrated pest management

Tomato is an important crop in Bangladesh and India, contributing to livelihoods and nutrition, but it is heavily affected by pests and diseases. This study analyzes pest and disease damage and farmers’ crop protection methods and quantified the potential economic gains of alternatives to chemical pesticides. Data come from a questionnaire survey of 744 tomato-producing farmers in India (Andhra Pradesh and Karnataka) and Bangladesh (Jashore). Farmers reported an average tomato yield loss from pests and diseases of 11.0 t/ha, of which Phthorimaea absoluta caused 2.1 t/ha. Farmers relied heavily on chemical pesticides, but also applied other methods. Better knowledge of pesticide health risks and beneficial insects and more use of alternative pest control methods were associated with lower pesticide use. Using an economic surplus model, we estimate ex-ante that the promotion of integrated pest management for the control of P. absoluta could generate economic gains of USD 264 million in Bangladesh and India over a 20-year period while insect resistant varieties could generate economic gains of more than USD 8.6 billion over this period.

Biosynthesized manganese oxide nanoparticles maintain firmness of tomato fruit by modulating soluble solids and reducing sugars under biotic stress

Nanoparticles have been reported to mitigate biotic and abiotic stresses in different fruits and vegetables. In this study, tomato fruit with black rot symptoms were collected and diagnosed. To control this disease, manganese oxide nanoparticles (MnO NPs) were synthesized in bacterial broth-culture. Based on microscopic, morphological, and genetic analyses, the pathogen causing black rot disease was identified as Aspergillus niger. MnO NPs were successfully synthesized in broth-culture of Bacillus subtilis, following the process of calcination and characterized. Fourier transform infrared (FTIR) spectrum revealed the existence of stabilizing and reducing agents (carboxylic acid, alkenes, and alkyl halides) on the surface of MnO NPs. X-Ray diffraction (XRD) analysis revealed the size (39 nm) and crystal-like nature of synthesized MnO NPs. Energy-dispersive X-ray spectroscopy (EDX) described the mass percentage of manganese (26.4%) and oxygen (23.3%). Scanning electron microscopy (SEM) displayed nearly spherical shape of MnO NPs and confirmed their nano-size. These MnO NPs exhibited significant mycelial growth inhibition of A. niger and notable control of tomato black rot disease of tomato. Though, all concentrations exhibited considerable effects, medium concentration of MnO NPs (2.5 mg/mL) performed best in both in vitro and in vivo analyses. At this concentration, tomato fruit maintained higher percentage of soluble solids, total sugars, reducing sugars and fruit firmness. These results proved very effective application of bacteria supplemented MnO NPs for the control of black rot disease of tomato. To our knowledge, this is the first study of tomato black rot, caused by A. niger in Pakistan.

“Tomato-Village”: a dataset for end-to-end tomato disease detection in a real-world environment

“Tomato-Village”: a dataset for end-to-end tomato disease detection in a real-world environment

Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage

The spread of infections and rot are crucial factors in the decrease in tomato production. Accurately segmenting the affected tomatoes in real-time can prevent the spread of illnesses. However, environmental factors and surface features can affect tomato segmentation accuracy. This study suggests an improved YOLOv8s-Seg network to perform real-time and effective segmentation of tomato fruit, surface color, and surface features. The feature fusion capability of the algorithm was improved by replacing the C2f module with the RepBlock module (stacked by RepConv), adding SimConv convolution (using the ReLU function instead of the SiLU function as the activation function) before two upsampling in the feature fusion network, and replacing the remaining conventional convolution with SimConv. The F1 score was 88.7%, which was 1.0%, 2.8%, 0.8%, and 1.1% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm, respectively. Meanwhile, the segment mean average precision (segment mAP@0.5) was 92.2%, which was 2.4%, 3.2%, 1.8%, and 0.7% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm. The algorithm can perform real-time instance segmentation of tomatoes with an inference time of 3.5 ms. This approach provides technical support for tomato health monitoring and intelligent harvesting.

Combined application of oligochitosan and Pichia carrbbica improves the disease resistance of postharvest tomato fruits

Our previous study showed that postharvest treatment of tomato with Pichia caribbica along with oligochitosan (OCH) significantly alleviated postharvest black spot of tomato fruit compared with P. caribbica alone. This study was to explore the effect of combined application of OCH and P. caribbica on disease resistance of tomato fruits through analyzing the activities of disease resistance-related enzymes, as well as transcriptome analysis of the fruit gene expression. Our findings indicated that P. caribbica, together with OCH, improved the activities of key enzymes associated with disease resistance in tomato fruit. Moreover, the combined application further upregulated the expression of genes involved in α-linolenic acid metabolism, the signal transduction pathways such as Ca2+, MAPK and plant hormone signaling pathways, resistant substance synthesis, and ROS metabolism, which improved the fruit disease resistance. Considering the significance of these results, OCH hold great potential for use in microbial biocontrol strategies against postharvest tomato diseases.

基于改进注意力机制的番茄叶部病害检测方法研究

The precise detection and recognition are the premise in accurate prevention and control of tomato diseases. To improve the accuracy of tomato diseases recognition model, nine kinds of sick leaves images including tomato target spot bacteria in Plant Village and healthy leaves images were used. A new attention mechanism module called CBAM-Ⅱ was created by changing the serial connection between Channel and Spatial attentions of CBAM to parallel connection, and then the results of two modules were added together. CBAM-Ⅱ had been verified to be effective and universal in the convolutional neural network model. The accuracy of MobileNet-V2 with CBAM-Ⅱ model was 99.47%，which had increased by 1.13%, 0.93%, 0.7%8 and 1.06 % respectively comparing with MobileNet-V2 model, MobileNet-V2 plus Channel attention module, MobileNet-V2 plus Spatial attention module, and CBAM attention module. Furthermore, the accuracy of AlexNet, Inception-V3 and ResNet50 model has increased 1.73, 0.15 and 0.33 % respectively when the CBAM-Ⅱ module was added. Results showed that the proposed module CBAM-Ⅱ created in this experiment is more effective in MobileNet-V2 model for tomato diseases recognition, and could solve interference problems resulted from the serial connection. Additionally, the accuracy of four convolutional neural network models including Mobilenet-V2, AlexNet, Inception-V3 and ResNet50 model had all increased when the CBAM-Ⅱ module was added, which represented the good universality of CBAM-Ⅱ module. The results could provide technical support in accurate detection and control of tomato diseases.

Antibacterial Efficacy of Green Synthesized Silver Nanoparticles Using Salvia nubicola Extract against Ralstonia solanacearum, the Causal Agent of Vascular Wilt of Tomato.

Ralstonia solanacearum is a phytopathogen causing bacterial wilt diseases of tomato and affecting its productivity, which leads to prominent economic losses annually. As an alternative to conventional pesticides, green synthesized nanoparticles are believed to possess strong antibacterial activities besides being cheap and ecofriendly. Here, we present the synthesis of silver nanoparticles (Sn-AgNPs) from medicinally important aqueous plant extracts of Salvia nubicola. Characterization of biologically synthesized nanoparticles was performed through UV-vis spectrophotometry, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy (TEM), and thermogravimetric analysis. The antibacterial activity of the biosynthesized silver nanoparticles was tested against the phytopathogen R. solanacearum through in vitro experiments. Preliminary phytochemical analysis of the plant extracts revealed the presence of substantial amounts of flavonoids (57.08 mg GAE/g), phenolics (42.30 mg GAE/g), tannins, and terpenoids. The HPLC phenolic profile indicated the presence of 25 possible bioactive compounds. Results regarding green synthesized silver nanoparticles revealed the conformation of different functional groups through FTIR analysis, which could be responsible for the bioreduction and capping of Ag ions into silver NPs. TEM results revealed the spherical, crystalline shape of nanoparticles with the size in the range of 23-63 nm, which validates SEM results. Different concentrations of Sn-AgNPs (T1 (500 μg/mL) to T7 (78.1 μg/mL)) with a combination of plant extracts (PE-Sn-AgNPs) and plant extracts alone exhibited an efficient inhibition of R. solanacearum. These findings could be used as an effective alternative preparation against the bacterial wilt of tomato.

Ecoepidemiological Model and Optimal Control Analysis of Tomato Yellow Leaf Curl Virus Disease in Tomato Plant

The purpose of this study is to analyze the impact of control strategies, namely, insecticide spray, roguing of a diseased tomato plant, and protective netting to protect tomato plant from tomato yellow leaf curl virus disease (TYLCVD). For this, we formulate and analyze a simple deterministic model for the transmission dynamics of TYLCVD that incorporates these control strategies. We initially take into account the constant control case, we calculate the basic reproduction number, and we investigate the existence and stability of the disease-free and endemic equilibria. We use the Kamgang-Sallet stability to ensure that the disease-free equilibrium point is globally asymptotically stable when the reproduction number R 0 is less than one. This indicates that TYLCVD dies out independent of the initial size of the tomato population. For R 0 < 1 , the disease-free equilibrium becomes unstable, and the endemic equilibrium is globally asymptotically stable. This indicates that TYLCVD propagates. In the nonconstant control case, we use Pontryagin’s maximum principle to derive the necessary conditions for the optimal control of the disease. Our findings show that all the combined efforts of two out of three strategies can significantly reduce the power of infectivity of the disease except the combination of the use of insecticide spray and rouging infected tomato plants. Besides our numerical simulations show, the implementation of the combination of roguing diseased plants and protective netting has a similar effect in minimizing or eliminating TYLCV as the use of all strategies. Hence, as resources are always in scarce, we recommend policymakers to adapt the combination of the use of roguing diseased tomato plants and protective netting to eradicate the disease.

Prediction of Disease in Tomato Leaves with use of Machine Learning Technique

India's sizable agricultural market provides the perfect conditions for cultivating a variety of products, including the tomato harvest. Detecting the transmission of diseases from unhealthy to healthy plants poses a severe threat to the agricultural industry because, if caught early enough, they can quickly spread and perhaps infest the entire farm. In terms of profit in good forming, early stage crop disease identification and severity monitoring is quite important. K-means clustering with fuzzy logic is used in the proposed study to evaluate the disease-affected region of the leaf and, as a result, assess the severity of the diseases. In this thesis, illnesses are detected using machine learning models for convolutional neural networks (CNN) and K-nearest neighbours (KNN).

Morphological characterization and molecular validation for ToLCV resistance (Ty-2 and Ty-3 genes) in tomato (Solanum lycopersicum)

An experiment was conducted during the rainy (kharif) season of 2019 and 2020 at ICAR-Indian Agricultural Research Institute, New Delhi to identify the potential genotypes having good horticultural traits along with strong and durable resistance against ToLCV disease of tomato (Solanum lycopersicum L.). A diverse set of 30 tomato genotypes carrying different Ty genes were screened out at phenotypic and genotypic level. First appearance/symptom of tomato leaf curl virus (ToLCV) disease was recorded in Pusa 120 only after 16 days of transplanting (DAT). Within 30 days of transplanting most of the genotypes, viz. Pusa 120, DT12, DT16, DT6 and Pusa Sheetal recorded 80 to 100 PDI (per cent disease incidence) and they were rated as highly susceptible to ToLCV. While at 90 DAT ToLCVincidence (≤15%) was observed in DT2, DT8, DT17, DT20 and DT30 and were categorized a resistant. None of the genotype was found as highly resistant. Four genotypes, viz. DT2, DT10, DT20 and DT30 showed presence of both resistant genes Ty-2 and Ty-3 at genotypic level. These promising genotypes will be useful as parental material to develop lines/hybrids carrying multiple genes against both monopartite and bipartite viruses for strong and durable resistance against ToLCV disease.

Isolation, characterization, and evaluation of putative new bacteriophages for controlling bacterial spot on tomato in Brazil.

Bacterial spot is a highly damaging tomato disease caused by members of several species of the genus Xanthomonas. Bacteriophages have been studied for their potential use in the biological control of bacterial diseases. In the current study, bacteriophages were obtained from soil and tomato leaves in commercial fields in Brazil with the aim of obtaining biological control agents against bacterial spot. Phage isolation was carried out by co-cultivation with isolates of Xanthomonas euvesicatoria pv. perforans, which was prevalent in the collection areas. In a host range evaluation, none of the phage isolates was able to induce a lytic cycle in all of the bacterial isolates tested. In in vivo tests, treatment of susceptible bacterial isolates with the corresponding phage prior to application to tomato plants led to a reduction in the severity of the resulting disease. The level of disease control provided by phage application was equal to or greater than that achieved using copper hydroxide. Electron microscopy analysis showed that all of the phages had similar morphology, with head and tail structures similar to those of viruses belonging to the class Caudoviricetes. The presence of short, non-contractile tubular tails strongly suggested that these phages belong to the family Autographiviridae. This was confirmed by phylogenetic analysis, which further revealed that they all belong to the genus Pradovirus. The phages described here are closely related to each other and potentially belong to a new species within the genus. These phages will be evaluated in future studies against other tomato xanthomonad strains to assess their potential as biological control agents.

CLASSIFICATION AND DETECTION OF TOMATO LEAF DISEASE

The tomato crop is a significant staple with a high commercial value on the Indian market and is produced in enormous quantities. Diseases are harmful to the health of the plant, which has an impact on its growth. It is essential to monitor the progress of the farmed crop to ensure minimal losses. Many different tomato diseases attack the crop's leaves at frightening rates. The primary goal of the proposed effort is to identify a simple method for detecting tomato leaf disease while employing limited computational resources to produce outcomes that are comparable to state-of- the-art. Automatic feature extraction is used by neural network models to help categorize the input image into the appropriate illness classifications. The average accuracy of this suggested system is between 94 to 95 percent, demonstrating the viability of the neural network approach even in challenging circumstances. Keywords: Agriculture, Convolution Neural Network, machine learning, Support Vector Machine.

Synergistic biocontrol of Bacillus subtilis and Pseudomonas fluorescens against early blight disease in tomato.

Early blight of tomato caused by Alternaria solani results in significant crop losses. In this study, Bacillus subtilis J3 and Pseudomonas fluorescens J8 were co-cultured as a synthetic microbial community (BCA) for synergistic biocontrol of A. solani, and the inhibition mechanism was investigated. BCA presented an inhibition ration against A. solani at 94.91%, which lowered the disease incidence by 38.26-42.87%; reduced peroxidase, catalase, superoxide dismutase activity of tomatoes by 73.11-90.22%; and promoted the biomass by 66.91-489.21%. With BCA protection, the relative expression of tomato resistance genes (including gPAL2, SWRKY, PR-10, and CHI) in roots and leaves was 12.83-90.70% lower than without protection. BCA also significantly altered the rhizosphere and phyllosphere microbial community. The abundance of potentially beneficial bacteria, including Bacillus, Pseudomonas, Arthrobacter, Lysobacter, and Rhizobium, elevated by 6.58-192.77%. They were negatively correlated with resistance gene expression, indicating their vital involvement in disease control. These results provided essential information on the synergistic biocontrol mechanism of bacteria against pathogens, which could contribute to developing novel biocontrol strategies. KEY POINTS: • Bacillus and Pseudomonas present a synergistic biocontrol effect against A. solani. • Biocontrol prevents pathogen damage and improves tomato growth and systemic resistance. • Beneficial bacteria thrive in the rhizosphere is the key to microbial regulation.

Hexaconazole and binary mixtures of it plus fludioxonil efficiently control tomato leaf mold caused by Cladosporium fulvum

Leaf mold caused by Cladosporium fulvum is one of the most severe foliar diseases of tomato. We were interested in whether we could use a binary mixture of hexaconazole and fludioxonil for the management of tomato leaf mold. The results showed that hexaconazole with an EC50 value of 0.30 μg/ml could significantly inhibit the mycelial growth of C. fulvum. A total of 126 isolates collected from Shandong Province were used to determine their sensitivity to hexaconazole, and the data fit a bimodal curve with a mean EC50 value of 4.69 μg/ml. The optimal proportion of the binary mixture of hexaconazole and fludioxonil against C. fulvum was 1:2, and its synergistic ratio (SR) was 2.23. Field trials in 2018 and 2019 indicated that both hexaconazole and the binary mixture of hexaconazole and fludioxonil (1:2) had favorable protective and curative efficacy against tomato leaf mold. Applying hexaconazole at 67.50 g/ha resulted in higher efficacy than did flusilazole at 75.00 g/ha, which had been registered in China to control tomato leaf mold. In addition, a binary mixture of hexaconazole and fludioxonil (1:2) at 60.75 g/ha had an efficacy of 85%, which was significantly higher than that of a commercial binary mixture of fluopyram and trifloxystrobin at 150.00 g/ha. Even when applied at 20.25 g/ha, hexaconazole could also provide better efficacy than thiophanate-methyl could regardless of whether it was applied before or after the occurrence of tomato leaf mold. In summary, hexaconazole and its binary mixture with fludioxonil (1:2) could be favorable candidates for the management of tomato leaf mold.

QTL and PACE analyses identify candidate genes for anthracnose resistance in tomato.

Anthracnose, caused by the fungal pathogen Colletotrichum spp., is one of the most significant tomato diseases in the United States and worldwide. No commercial cultivars with anthracnose resistance are available, limiting resistant breeding. Cultivars with genetic resistance would significantly reduce crop losses, reduce the use of fungicides, and lessen the risks associated with chemical application. A recombinant inbred line (RIL) mapping population (N=243) has been made from a cross between the susceptible US28 cultivar and the resistant but semiwild and small-fruited 95L368 to identify quantitative trait loci (QTLs) associated with anthracnose resistance. The RIL population was phenotyped for resistance by inoculating ripe field-harvested tomato fruits with Colletotrichum coccodes for two seasons. In this study, we identified twenty QTLs underlying resistance, with a range of phenotypic variance of 4.5 to 17.2% using a skeletal linkage map and a GWAS. In addition, a QTLseq analysis was performed using deep sequencing of extreme bulks that validated QTL positions identified using traditional mapping and resolved candidate genes underlying various QTLs. We further validated AP2-like ethylene-responsive transcription factor, N-alpha-acetyltransferase (NatA), cytochrome P450, amidase family protein, tetratricopeptide repeat, bHLH transcription factor, and disease resistance protein RGA2-like using PCR allelic competitive extension (PACE) genotyping. PACE assays developed in this study will enable high-throughput screening for use in anthracnose resistance breeding in tomato.

A Brief Evaluation of a Copper Resistance Mobile Genetic Island in the Bacterial Leaf Spot Pathogen Xanthomonas euvesicatoria pv. perforans.

Due to the continuous use of copper containing bactericides without effective alternative bactericides, copper resistance has become more prevalent in plant pathogens, including Xanthomonas euvesicatoria pv. perforans (formerly Xanthomonas perforans), a predominant cause of bacterial leaf spot disease of tomato and pepper in the Southeastern United States. Previously, reports of copper resistance have been associated with a large conjugative plasmid. However, we have characterized a copper resistance genomic island located within the chromosome of multiple X. euvesicatoria pv. perforans strains. The island is distinct from a previously described chromosomally encoded copper resistance island in X. vesicatoria strain XVP26. Computational analysis revealed the genomic island to contain multiple genes associated with genetic mobility, including both phage-related genes and transposase. Among copper-tolerant strains of X. euvesicatoria pv. perforans isolated from Florida, the majority of strains were found to have the copper resistance chromosomally encoded rather than plasmid borne. Our results suggest that this copper resistance island may have two modes of horizontal gene transfer and that chromosomally encoded copper resistance genes may provide a fitness advantage over plasmid-borne resistance.