Disease Of Tomato Research Articles

Cloning and Characterization of Rep Gene of the Strain of Tomato Leaf Curl New Delhi Virus (G: Begomovirus, F: Geminiviridae) Infecting Ridge Gourd in Karnataka and in silico Analysis of Rep Protein

Ridge gourd [Luffa acutangula (Roxb.) L.,] is one of the important vegetable crop affected by various diseases particularly viral disease tomato leaf curl New Delhi virus (ToLCNDV). The ridge gourd plants with leaf curling and yellowing symptoms were collected and used in for the amplification of the of the Rep gene. Cloning, sequencing and bioinformatics analysis of the Rep gene was carried out. The results revealed the association of this virus isolate with the different host of Cucurbitaceae family. Pairwise identity derived implemented by BLAST identified that the replicator initiator protein gene (Rep) of the bipartite virus showed maximum 96.90% sequence identity to the corresponding Rep gene of ridge gourd tomato leaf curl New Delhi virus isolate RG3 in ridge gourd - complete genome from Madikari, Karnataka (accession no KT426905). This also showed 97.90% sequence similarities with tomato leaf curl New Delhi virus strain CH7 of Chrysanthemum. Phylogenetic dendrogram of the Rep gene depicted that it grouped with ToLCNDV of different Indian isolates of ridge gourd and with ToLCNDV of potato from Pakistan (LN908936), and with tomato from Bangladesh (KM383738.1) showing 93.12% and 92.94% sequence similarity respectively. This study provides association of the Rep gene infected ridge gourd pants with Cucurbitaceae family crops and other related crops. This will helps in carrying further research in the management of ToLCNDV diseases using different biotechnological applications.

Molecular and genetic basis of tomato resistance to major fungal diseases

This review presents an analysis of the literature, which indicates the popularity of breeding methods in the fight against oomycete and fungal diseases of tomato: late blight (pathogen − Phytophthora infestans (Mont.) de Bary), fusarium wilt (pathogen − Fusarium oxysporum f. sp. lycopersici (Sacc.) Snyder and Hansen), early blight or alternariosis (pathogens − representatives of the genus Alternaria), cladosporiosis (pathogen−Cladosporium fulvum Cook.). Theoretical and practical achievements, the latest developments in genetics, molecular biology, plant physiology, microbiology are considered. Special attention is paid to modern information about the diversity of tomato pathogens, their morphological, genetic, physiological features, including racial composition; the presence of pathogen resistance genes in the gene pool of the culture under study, their mapping and the possibility of introduction into the genome from other sources; about the developed markers of target genes and loci of quantitative traits; the degree of associations between a molecular marker and a target gene; about the features of inheritance of the studied trait, as well as the possibility of pyramiding R-genes and QTL in one genotype. To accelerate and improve the efficiency of tomato breeding for resistance to pathogens, these issues are of great importance. Their study will strengthen the integration of modern biotechnology with the traditional breeding process, which is carried out by classical methods.

The potentiality of Wickerhamomyces anomalus against postharvest black spot disease in cherry tomatoes and insights into the defense mechanisms involved

Our previous study revealed that Wickerhamomyces anomalus effectively inhibited Botrytis cinerea disease in cherry tomatoes. However, the potentiality of W. anomalus against black spot disease in cherry tomatoes as well as possible mechanisms related to this efficacy remain unclear. In our current study, we investigated the efficacy of W. anomalus against black spot disease in cherry tomatoes, and the molecular mechanisms related to disease-resistance response of W. anomalus treated cherry tomatoes were also investigated by transcriptome analysis. The results exhibited that W. anomalus successfully controlled postharvest black spot disease of cherry tomatoes caused by Alternaria alternata. Furthermore, W. anomalus vigorously controlled the spore germination and reduced the germ tube elongation of A. alternata. The physiological mechanisms by which W. anomalus inhibited the black spot decay in cherry tomatoes mainly through enhancing the ascorbate peroxidase (APX) and chitinase (CHI), and reducing the content of malondialdehyde (MDA). In addition, based on transcriptome technology, W. anomalus could up-regulate genes involved in several important KEGG pathways, including amino sugar and nucleotide sugar metabolism pathway, selenocompound metabolism pathway, linoleic acid metabolism pathway, and galactose metabolism pathway. These genes may help the fruit to enhance the enzyme activities and nutritional values of cherry tomatoes, synthesize the regulatory molecules in plant disease response as well as plant growth and development, increase the defense and oxidation resistance of the fruit during and after harvesting, and trigger ROS induced resistance in cherry tomatoes. Ultimately, W. anomalus has been proved as a biocontrol agent which could effectively control black spot disease in cherry tomatoes caused by A. alternata. This study can provide theoretical reference for the practical application of antagonistic yeasts in the biological control of postharvest diseases of cherry tomatoes.

Evaluation of the Humic Acid Efficiency and the Bioagent Chaetomium globosum in Controlling Fusarium Wilt Disease in Tomato

Fusarium wilt disease presents a significant menace to tomato plants, necessitating effective preventative measures. Five isolates of Fusarium oxysporum f.sp. lycopersici from infected tomato plants from different regions in Missan province, each showing varying levels of pathogenicity. Differences in the ability of tomato seedlings to be infected by these isolates were also observed during a pot experiment, ranging from highly pathogenic to non-pathogenic. To address this issue, the efficacy of the bioagent Chaetomium globosum and humic acid in controlling the pathogenic fungus F. oxysporum f.sp. lycopersici and improving the growth indicators of tomato plants in the presence of the causative fungus. Two concentrations of humic acid (8 and 4 mL/l) were tested to determine their efficacy in reducing the growth of pathogenic fungi in vitro. We found that the growth of the pathogenic fungi decreased to 41.06% at a concentration of 8 ml/l, compared to the control, which showed no growth. Moreover, when used alone or in combination with humic acid, C. globosum significantly reduced the percentage and severity of infection. The treatment involving the interaction between the bioagent and humic acid at a concentration of 8 ml/l recorded an infection percentage of 14.4% and an infection severity of 11.87%, compared to the pathogenic fungus treatment that resulted in 53% infection and 43.80% severity, respectively. Moreover, the use of C. globosum and different concentrations of humic acid raised all growth indices in all treatments whether in the presence or absence of pathogenic funus. These improvements included increased fresh and dry weight of the shoot and root, enhanced plant height and leaf area, as well as a significant increase in the content of total phenols and total chlorophyll in the leaves. The findings of this study highlight the potential of C. globosum and humic acid as effective control measures against fusarium wilt disease in tomato plants. The results indicate their ability to suppress the pathogenic fungus and promote plant growth. These findings hold promise for the development of sustainable agricultural practices.

Classification of diseases in tomato leaves with Deep Transfer Learning

Plant diseases are important factors because they significantly affect the quality, quantity, and yield of agricultural products. Therefore, it is important to detect and diagnose these diseases at an early stage. The overall objective of this study is to develop an acceptable deep learning model to correctly classify diseases on tomato leaves in RGB color images. To address this challenge, we use a new approach based on combining two deep learning models VGG16 and ResNet152v2 with transfer learning. The image dataset contains 55 000 images of tomato leaves in 5 different classes, 4 diseases and one healthy class. The results of our experiment are promising and encouraging, showing that the proposed model achieves 99,08 % accuracy in training, 97,66 % in validation, and 99,0234 % in testing

A deep learning-based mobile app system for visual identification of tomato plant disease

<span lang="EN-US">Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.</span>

High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization

Deep learning and machine learning are cutting-edge methods for analysing images that have considerable potential. Artificial Neural Networks (A-NNs), one of the most well-known methods of computer intelligence, are now used in machine learning (ML) and deep transfer learning (DL) to raise plant production and quality. Identification and primary prevention of plant diseases at the appropriate time are essential for boosting productivity. Due to the phenomenon of minimally intense data in the background and foreground areas of the image, the extensive colour similarity between regions of unhealthy and normal leaves, the presence of noise in the sampling data, and changes in the location, size, and shape of plant leaf, it is difficult to correctly identify and classify plant diseases. In an effort to address these issues, a reliable technique for classifying plant diseases was developed by using a deep AlexNet CNN architecture as the main network with batch normalisation. In the three-step process, the first annotation is made to obtain the RoI (region of interest). The AlexNet CNN is therefore suggested for deep primary feature extraction in a constructed efficient network. The research demonstrates that the existing strategy is superior to more recent ones in terms of accuracy and dependability in recognising diseases in plants. Based on a deep transfer AlexNet CNN model, this research work developed a model for diseases identification and classification in plant leaves. It is trained using additional datasets that include a variety of plant leaf classifications and background images. From Plant Village and Kaggle, we gathered data on healthy and diseased tomato plant leaves. We are obtaining a near-balanced dataset containing ten different leaf disease kinds, such as bacterial, fungal, viral, and nutrient insufficiency. Ten classes have been considered for this research by gathering a dataset with associated images of the typical and abnormal tomato plant leaves. Considered in this work were the various labels for healthy and diseased tomato leaves, such as early blight, Bacterial spot, late bright mold, healthy, etc. Since deep CNN models have shown notable machine vision results, they are used in this case to diagnose and categorise plant illnesses from their leaves. As a result, the proposed CNN models can thus now be evaluated from confusion matrix using data analysis criteria, primarily focusing on metrics for evaluation like training and validation accuracy, loss, Recall, Precision, F1 score, processing speed, and performance.

Isolation and Identification of Fungi Associated with Fruit Rot Disease of Tomato (Solanum lycopersicum L.) in the Southern Guinea Savannah, Nigeria

Rot disease is a major threat to tomato fruit production and postharvest handling in major tomato growing areas in Nigeria. Rotted tomato fruits were randomly collected from farmers’ farms for the purpose of isolation and identification of rot causing fungi in Tarka, Benue State, Nigeria between July and December, in 2015 and 2016. Results revealed that Aspergillus flavus, A. niger, Alternaria solani, Phytophthora infestans, Oidium neolycopersici, Sclerotium rolfsii, Fusarium oxysporum, Verticillium spp and Colletotrichum spp caused tomato fruit rot in the area. In both years, the lowest number of fungi was recorded in the months of July and December while the highest number was obtained in September. The mean number of fungi isolated showed that S. rolfsii was the least with 3.17 in 2015 and 1.66 in 2016 while F. oxysporum was the highest with 14.33 in 2015 and 9.33 in 2016. There were more fungi isolated in 2015 than in 2016. There were significant differences (P≤0.05) in the mean number of A. niger, P. infestans, O. neolycopersici and F. oxysporum isolated in both years. In conclusion, farmers should handle tomato fruits with care, to reduce postharvest injury and damage in order to ensure the availability of disease free fruits and to increase the output for the global population.

First specific detection and validation of tomato wilt caused by Fusarium brachygibbosum using a PCR assay.

Tomato wilt is a widespread soilborne disease of tomato that has caused significant yield losses in many tomato growing regions of the world. Previously, it was reported that tomato wilt can be caused by many pathogens, such as Fusarium oxysporum, Ralstonia solanacearum, Ralstonia pseudosolanacearum, Fusarium acuminatum, and Plectosphaerella cucumerina. In addition, we have already reported that Fusarium brachygibbosum caused symptomatic disease of tomato wilt for the first time in China. The symptoms of tomato wilt caused by these pathogens are similar, making it difficult to distinguish them in the field. However, F. brachygibbosum specific identification method has not been reported. Therefore, it is of great importance to develop a rapid and reliable diagnostic method for Fusarium brachygibbosum to establish a more effective plan to control the disease. In this study, we designed F. brachygibbosum-specific forward primers and reverse primers with a fragment size of 283bp located in the gene encoding carbamoyl phosphate synthase arginine-specific large chain by whole genome sequence comparison analysis of the genomes of eight Fusarium spp.. We then tested different dNTP, Mg2+ concentrations, and annealing temperatures to determine the optimal parameters for the PCR system. We evaluated the specificity, sensitivity and stability of the PCR system based on the optimized reaction system and conditions. The PCR system can specifically identify the target pathogens from different fungal pathogens, and the lower detection limit of the target pathogens is at concentrations of 10 pg/uL. In addition, we can accurately identify F. brachygibbosum in tomato samples using the optimized PCR method. These results prove that the PCR method developed in this study can accurately identify and diagnose F. brachygibbosum.

Volatile Formulation of Mentha spicata Induced Defense Mechanism in Tomato and Suppressed Damping Off Disease Caused by Pythium aphanidermatum

Volatile Formulation of Mentha spicata Induced Defense Mechanism in Tomato and Suppressed Damping Off Disease Caused by Pythium aphanidermatum

The Pseudomonas syringae pv. tomato DC3000 effector HopD1 interferes with cellular dynamics associated with the function of the plant immune protein AtNHR2B

The plant pathogenic bacterium Pseudomonas syringae pv tomato DC3000 (Pst DC3000) causes disease in tomato, in the model plant Arabidopsis thaliana, and conditionally in Nicotiana benthamiana. The pathogenicity of Pst DC3000 is mostly due to bacterial virulence proteins, known as effectors, that are translocated into the plant cytoplasm through the type III secretion system (T3SS). Bacterial type III secreted effectors (T3SEs) target plants physiological processes and suppress defense responses to enable and support bacterial proliferation. The Pst DC3000 T3SE HopD1 interferes with plant defense responses by targeting the transcription factor NTL9. This work shows that HopD1 also targets the immune protein AtNHR2B (Arabidopsis thaliana nonhost resistance 2B), a protein that localizes to dynamic vesicles of the plant endomembrane system. Live-cell imaging of Nicotiana benthamiana plants transiently co-expressing HopD1 fused to the epitope haemagglutinin (HopD1-HA) with AtNHR2B fused to the red fluorescent protein (AtNHR2B-RFP), revealed that HopD1-HA interferes with the abundance and cellular dynamics of AtNHR2B-RFP-containing vesicles. The results from this study shed light into an additional function of HopD1 while contributing to understanding how T3SEs also target vesicle trafficking-mediated processes in plants.

Tomato brown rot disease detection using improved YOLOv5 with attention mechanism.

Brown rot disease poses a severe threat to tomato plants, resulting in reduced yields. Therefore, the accurate and efficient detection of tomato brown rot disease through deep learning technology holds immense importance for enhancing productivity. However, intelligent disease detection in complex scenarios remains a formidable challenge. Current object detection methods often fall short in practical applications and struggle to capture features from small objects. To overcome these limitations, we present an enhanced algorithm in this study, building upon YOLOv5s with an integrated attention mechanism for tomato brown rot detection. We introduce a hybrid attention module into the feature prediction structure of YOLOv5s to improve the model's ability to discern tomato brown rot objects in complex contexts. Additionally, we employ the CIOU loss function for precise border regression. Our experiments are conducted using a custom tomato disease dataset, and the results demonstrate the superiority of our enhanced algorithm over other models. It achieves an impressive average accuracy rate of 94.6% while maintaining a rapid detection speed of 112 frames per second. This innovation marks a significant step toward robust and efficient disease detection in tomato plants.

Reversible phosphorylation of a lectin-receptor-like kinase controls xylem immunity

Pattern-recognition receptors (PRRs) mediate basal resistance to most phytopathogens. However, plant responses can be cell type specific, and the mechanisms governing xylem immunity remain largely unknown. We show that the lectin-receptor-like kinase LORE contributes to xylem basal resistance in Arabidopsis upon infection with Ralstonia solanacearum, a destructive plant pathogen that colonizes the xylem to cause bacterial wilt. Following R.solanacearum infection, LORE is activated by phosphorylation at residue S761, initiating a phosphorelay that activates reactive oxygen species production and cell wall lignification. To prevent prolonged activation of immune signaling, LORE recruits and phosphorylates type 2C protein phosphatase LOPP, which dephosphorylates LORE and attenuates LORE-mediated xylem immunity to maintain immune homeostasis. A LOPP knockout confers resistance against bacterial wilt disease in Arabidopsis and tomatoes without impacting plant growth. Thus, our study reveals a regulatory mechanism in xylem immunity involving the reversible phosphorylation of receptor-like kinases.

Evaluating the impact of tuned pre-trained architectures' feature maps on deep learning model performance for tomato disease detection

Evaluating the impact of tuned pre-trained architectures' feature maps on deep learning model performance for tomato disease detection

Medicinal Plant-Derived Copper Nanoparticles Effectively Manage Early Blight Disease in Tomato: An In vitro Study

Aims: Early blight of tomato (Solanum lycopersicum L.) incited by Alternaria solani is an economically important disease. This paper analyses the efficacy of copper nanoparticles (Cu-NPs) as an alternative to its existing management practices that predominantly rely on toxic fungicides which are potentially harmful to humans and the environment.
 Study Design: The experiment was conducted in a completely randomized block design with 7 treatments viz. T1:Cu-NPs from tulsi leaves, T2:Cu-NPs from neem leaves, T3:Cu-NPs from bael leaves, T4: Cu-NPs from chemical A (prepared from 0.2 M of Cu(NO3)2.3H2O + 0.2 M Ascorbic acid), T5: Cu-NPs from chemical B (0.2 M Cu(NO3)2.3H2O + 0.2 M CuCl2 + 0.4 N NH4OH), T6: Carbendazim 50 WP as check and T7: Control. Each treatment was tested at two levels of in vitro concentrations (i.e. 300 and 500 ppm) and the treatments were replicated thrice.
 Place and Duration of Study: Considering the importance of the disease and for developing an effective management strategy, the present study was carried out in the Department of Plant Pathology, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar during 2020-21.
 Methodology: Stable Cu-NPs were synthesized through three green synthesis methods by using bael (Agel marmelos L.), tulsi (Ocimum sanctum L.), and neem (Azadirachta indica L.) leaves and two chemical synthesis methods (i.e. chemical A and B).
 Results: The size (diameter) of the Cu-NPs from Agel marmelos, Ocimum sanctum, and Azadirachta indica were 427.4, 1,019, and 246.5 nm, and that from the aqueous solution reduction method and precipitation method were 1,537 and 867.8 nm, respectively. The synthesized Cu-NPs were evaluated against Alternaria solani at 300 and 500 ppm while Carbendazim 50 WP served as the standard check. The Azadirachta indica leaf-derived Cu-NPs at 300 and 500 ppm in the in vitro potato dextrose ager (PDA) medium exhibited the maximum mycelial growth inhibition of 60.24% and 75.9%, respectively. The size of Cu-NPs showed an inverse relation with the antifungal efficacy that indicated greater cellular penetration of smaller nanoparticles whereas much lower inhibition of mycelial growth in Carbendazim 50 WP as a check showed the lowest growth inhibition of 39.75% (300 ppm) and 59.03% (500 ppm).
 Conclusion: The results thus indicated higher efficacy of nanoformulations at lower concentrations that can be effectively used for the development of new nano-based plant protection agents against the early blight of tomatoes.

Evaluation of local movement of Bemisia tabaci MEAM1 and tomato severe rugose virus transmission to tomato plants using marked and unmarked whiteflies

AbstractTomato golden mosaic disease, caused by the tomato severe rugose virus (ToSRV), is one of the most serious tomato diseases. ToSRV is transmitted by the whitefly Bemisia tabaci MEAM1, from a wide range of hosts. Introducing ToSRV in commercial fields probably can occur by transporting the virus from distant locations. However, our hypothesis is that the inoculum causing epidemic development might be related to nearby sources. This study evaluated the short‐range flight distance of B. tabaci MEAM1 (marked with detectable proteins and powder dye), through recaptures in yellow sticky traps, and the transmission of ToSRV by viruliferous B. tabaci MEAM1 to tomato plants placed at increasing distances from a release point. Evaluated whiteflies reached a maximum of 30 m from the release point, and no ToSRV‐infected tomato plants were detected at greater distances. These findings reinforce the suspicion that the epidemic‐supporting source of ToSRV inoculum must be close to commercial cultivation areas. About the efficacy of the type of markers used, of the two protein sources tested (pasteurized chicken egg‐white albumin and commercial soy‐based extracts as sources of albumin and lecithin, respectively), albumin was considered easier to detect. Marking with these proteins or with two dyes (blue or pink) did not affect B. tabaci MEAM1 mortality, although field tests showed that fewer marked than unmarked insects were recaptured. Dye marking was considered more practical because of the ease of detection. The study validates a new tool to understand the movement of B. tabaci MEAM1, based on different marking methods.

Sensitivity of tomato leaf mould–causing Fulvia fulva to seven succinate dehydrogenase inhibitor (SDHI) fungicides in Nara Prefecture, Japan and high efficacy of isofetamid in controlling SDHI‐resistant isolates

AbstractLeaf mould caused by Fulvia fulva is a major disease of tomato in Japan. Resistance to several types of fungicides, including succinate dehydrogenase inhibitors (SDHIs), has been reported in isolates from Japan. In this study, we evaluated the sensitivity of F. fulva to SDHI fungicides, SDHI fungicide efficacy in controlling sensitive and resistant isolates, and the presence of mutations in the Sdh B gene, the target site of SDHI fungicides. The minimum inhibitory concentration (MIC) of six SDHI fungicides, excluding isofetamid, ranged from 0.1 to >100 μg/mL against mycelial growth of 45 F. fulva isolates collected in Nara prefecture, Japan, whereas that of isofetamid ranged from 0.1 to only 10 μg/mL. The peak MIC distribution of fluopyram and isofetamid was 10 μg/mL, that of inpyrfluxam was 100 μg/mL and was >100 μg/mL for the remaining SDHI fungicides. In a greenhouse inoculation experiment, only isofetamid controlled the highly resistant isolates with an MIC of 10 μg/mL and the remaining fungicides had MIC >100 μg/mL. In addition, moderately and highly resistant isolates were controlled by the application of isofetamid 6 h or 7 days before inoculation or 3 days after inoculation. Mutations in the Sdh B gene were not observed among sensitive, moderately resistant and highly resistant isolates, suggesting that mutations in other Sdh genes and/or changes in efflux pump expression were involved. Our results indicate the presence of cross‐resistance and different levels of sensitivity reduction among SDHI fungicides. Isofetamid showed the lowest resistance development among the tested SDHI fungicides and is considered effective in controlling tomato leaf mould.

Mobile app-based tomato disease identification with fine-tuned convolutional neural networks

Rapid detection and diagnosis of plant diseases is crucial for healthy crop growth and maintaining harvest quality. Many diseases, such as bacterial spot, late blight, and leaf mould, can affect tomato cultivation. In this paper, a mobile app-based system for intelligent disease detection on tomato leaves is developed using convolutional neural networks (CNNs). The study uses transfer learning approaches to fine-tune well-known CNN architectures like AlexNet, ResNet-50, SqueezeNet-1.1, VGG19, and DenseNet-121 on a dataset made up of images of tomato leaf from the PlantVillage dataset as well as self-captured images. Over 95% accuracy is attained by all trained CNN models, with DenseNet-121 leading the pack with a stellar 99.85%. Consequently, DenseNet-121 is proposed for use with a user-friendly mobile application to detect disease in tomato leaves. A robust user interface in English and two important Indian languages makes this application more accessible to a wider user base.

Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.

Inhibition of pathogen and induction of resistance against early blight: Alternaria solani Sorauer in tomato plants (Solanum lycopersicum L.) through external application of compounds from seaweed Chaetomorpha antennina Bory

BackgroundThe indiscriminate application of pesticides has depleted soil fertility. Marine algae are regarded as excellent sources of bioactive compounds that can stimulate plant growth and also enhance their resistance against biotic and abiotic stressors. Compounds derived from the marine algae Chaetomorpha antennina (Cladophoraceae) were analysed for their biocontrol efficiency against the tomato disease pathogen Alternaria solani as an alternate technique to preserve agricultural output sustainability.ResultsThe active fraction from the methanol extract of C. antennina (CA-F8) proved effective against the pathogen in vitro by affecting A. solani's spore germination (92.13%) along with their mycelial growth thereby decreasing their dry weight (44.71%). GC–MS examination revealed the presence of fatty acids (defence pathway activators), terpenes (antimicrobial compound) and antioxidants (deactivators of pathogen enzymes). Seaweed compounds also elicited polyphenol oxidase (PPO) (78.8%) and peroxidase (PO) (54.6%) synthesis, along with stimulating the accumulation of foliar phenols (67.5%) that are components of jasmonic acid defense pathway, higher compared to control plants. Salicylic acid was detected only in treated plants, thereby offering efficient disease control.ConclusionsBy triggering natural systemic defences, salicylate (SA) and jasmonate (JA) mediated pathways; the study demonstrates the elicitor potentials of chemicals from C. antennina that was efficient in controlling early blight (EB) disease. In addition to providing a different method for crop protection to reduce or replace the demand for chemical pesticides, this study reveals unique projections.Graphical