Diseased Tomato Plants Research Articles

The tomato resistance gene Bs4 suppresses leaf watersoaking phenotypes induced by AvrHah1, a transcription activator-like effector from tomato-pathogenic xanthomonads.

The Xanthomonas transcription activator-like effector (TALE) protein AvrBs3 transcriptionally activates the executor-type resistance (R) gene Bs3 from pepper (Capsicum annuum), thereby triggering a hypersensitive cell death reaction (HR). AvrBs3 also triggers an HR in tomato (Solanum lycopersicum) upon recognition by the nucleotide-binding leucine-rich repeat (NLR) R protein Bs4. Whether the executor-type R protein Bs3 and the NLR-type R protein Bs4 use common or distinct signalling components to trigger an HR remains unclear. CRISPR/Cas9-mutagenesis revealed, that the immune signalling node EDS1 is required for Bs4- but not for Bs3-dependent HR, suggesting that NLR- and executor-type R proteins trigger an HR via distinct signalling pathways. CRISPR/Cas9-mutagenesis also revealed that tomato Bs4 suppresses the virulence function of both TALEs, the HR-inducing AvrBs3 protein and of AvrHah1, a TALE that does not trigger an HR in tomato. Analysis of AvrBs3- and AvrHah1-induced host transcripts and disease phenotypes in CRISPR/Cas9-induced bs4 mutant plants indicates that both TALEs target orthologous transcription factor genes to promote disease in tomato and pepper host plants. Our studies display that tomato mutants lacking the TALE-sensing Bs4 protein provide a novel platform to either uncover TALE-induced disease phenotypes or genetically dissect components of executor-triggered HR.

Design a mobile application to detect tomato plant diseases based on deep learning

Plant diseases consider the most dominant matter for farmers' concerns becausethe operation of discovering and dealing with them requires accuracy, experience, and time. Therefore, this paper proposes an approach to classify seven varieties of tomato diseases using deep learning models. A dataset of 10448 images from PlantVillage and google utilize to train the deep learning (CNN models). The trained models proved their ability to classify with high accuracy, as the highest testing accuracy reached 95.71% for the proposed model for 50 epochs only. The resulted best model is published to a mobile application using the android studioplatform, this application enables the farmer to classify plant diseases accurately and easily. The proposed model and mobile application could be extended to classify as many plant diseases as possible.

Effect of Methanolic Extracts of Senna didymobotrya and Moringa oleifera on Growth of Ralstonia solanacearum

Tomato is one of the most important vegetables in the world. Ralstonia solanacearum causes a devastating bacterial wilt that is wide-spread throughout tropical environments that has been difficult to control with chemicals and African cultural practices in poor regions of the world. This study was initiated to determine the inhibitory effect of methanolic root extract of Senna didymobotrya and Moringa oleifera seed extracts on growth and development of R. solanacearum. The study was conducted both in the Laboratory and in the green house in Maseno from where S. didymobotrya and M. oleifera specimens used were collected from the University Botanic Garden, dried under shade for 30 days, before being ground into a fine powder. 1000 grams of the plant powder was later transferred into a conical flask and covered with 250mls of methanol. Filtration was done and the extracts concentrated. Ten diseased tomato plants showing bacterial wilt symptoms earlier collected from Maseno, Mariwa, Seme and Holo all within Kisumu county (Kenya) were cut to obtain plant sections (0.5–1cm) that were later plated onto Triphenyltetrazolium chloride media. Bacteriostatic activity of the extracts was determined by the disc diffusion method on Mueller Hinton agar. In vivo evaluations were conducted in a green house using tomato seedlings. Data collected was subjected to analysis of variance and means separated were compared using least significance difference (P≤ 0.05). Both plants extracts showed inhibitory activity against R. solanacearum pathogen (M. Oleifera; 8.5 and S. didymobotrya 9.2). Based on the outcome of this study, S. didymobotrya is recommended as a potential botanical agent suitable for future trials and use in the control of R. solanacerum by tomato farmers in Maseno region Kisumu county.

A New Compact Method Based on a Convolutional Neural Network for Classification and Validation of Tomato Plant Disease

With recent advancements in the classification methods of various domains, deep learning has shown remarkable results over traditional neural networks. A compact convolutional neural network (CNN) model with reduced computational complexity that performs equally well compared to the pretrained ResNet-101 model was developed. This three-layer CNN model was developed for plant leaf classification in this work. The classification of disease in tomato plant leaf images of the healthy and disease classes from the PlantVillage (PV) database is discussed in this work. Further, it supports validating the models with the images taken at “Krishi Vigyan Kendra Narayangaon (KVKN),” Pune, India. The disease categories were chosen based on their prevalence in Indian states. The proposed approach presents a performance improvement concerning other state-of-the-art methods; it achieved classification accuracies of 99.13%, 99.51%, and 99.40% with N1, N2, and N3 models, respectively, on the PV dataset. Experimental results demonstrate the validity of the proposed approach under complex background conditions. For the images captured at KVKN for predicting tomato plant leaf disease, the validation accuracy was 100% for the N1 model, 98.44% for the N2 model, and 96% for the N3 model. The training time for the developed N2 model was reduced by 89% compared to the ResNet-101 model. The models developed are smaller, more efficient, and less time-complex. The performance of the developed model will help us to take a significant step towards managing the infected plants. This will help farmers and contribute to sustainable agriculture.

Phylogenetic Study of Alternaria Potato and Tomato Pathogens in Russia

Early blight (EB) is a destructive disease affecting potato and tomato plants in Russia, caused by a heterogeneous group of plant pathogenic Alternaria fungi. The current species delimitation in Alternaria sect. Porri with medium to large conidia and a long (filamentous) beak is based on molecular data. In this study, the ITS, GAPDH, RPB2, TEF1, and Alt a 1 gene regions were analyzed in 41 large-spored Alternaria isolates obtained from diseased potato and tomato plants collected from 13 regions in Russia. Our data revealed five pathogenic species (A. alternariacida, A. grandis, A. linariae, A. protenta, and A. solani). Two species (A. solani and A. linariae) were found to be associated with early blight of tomato. Alternaria linariae and A. protenta were confirmed as the major causal agents of tomato and potato early blight, respectively. There were no phylogenetic groupings among tested Russian Alternaria isolates associated with their locality.

Plantago lagopus extract as a green fungicide induces systemic resistance against Rhizoctonia root rot disease in tomato plants.

Extensive use of chemical control agents and fungicides typically leads to numerous risks to human health and the environment. Using plant extracts as natural substances represents a dual key for the environment and sustainable food production, as it reduces the input of synthetic pesticides into the environment and/or controls plant pathogens. For the first time, a Plantago lagopus ethanolic extract has been characterized and evaluated for its protective and curative effects against Rhizoctonia solani in tomato plants. The results showed that P. lagopus extract (10 μg/ml) completely inhibited R. solani mycelial growth in vitro. At 20 days of post fungal inoculation, the results demonstrated that using P. lagopus extract (100 μg/ml) in vivo enhanced tomato plant growth by significantly increasing shoot and root parameters in protective and curative treatments. Furthermore, the protective and curative treatments significantly reduced the disease index by 18.66 and 38.66%, respectively. Induction of systemic resistance with upregulation of PR-1 and PR-2 and a significant increase in the transcriptional levels of PR-3 and CHS in all P. lagopus extract-treated tomato plants were reported compared to untreated plants. HPLC analysis showed that the most common polyphenolic components detected in P. lagopus extract were rutin (74206.3 mg/kg), naringenin (2388.74 mg/kg), quercetin (1249.13 mg/kg), and p-hydroxybenzoic acid (1035.87 mg/kg). In addition, the ellagic acid (798.47 mg/kg), vanillic acid (752.55 mg/kg), catechol (648.89 mg/kg), cinnamic acid (332.51 mg/kg), ferulic acid (296.32 mg/kg), benzoic acid (295.95 mg/kg), and chlorogenic acid (116.63 mg/kg) were also reported. Our study is the first to show that P. lagopus extract can help plants fight off R. solani fungal infection. Furthermore, the findings imply that using the P. lagopus extract as a natural biocontrol agent could be a sustainable strategy to manage plant fungal diseases.

Recognition of a translocation motif in the regulator HpaA from Xanthomonas euvesicatoria is controlled by the type III secretion chaperone HpaB.

The Gram-negative plant-pathogenic bacterium Xanthomonas euvesicatoria is the causal agent of bacterial spot disease in pepper and tomato plants. Pathogenicity of X. euvesicatoria depends on a type III secretion (T3S) system which translocates effector proteins into plant cells and is associated with an extracellular pilus and a translocon in the plant plasma membrane. Effector protein translocation is activated by the cytoplasmic T3S chaperone HpaB which presumably targets effectors to the T3S system. We previously reported that HpaB is controlled by the translocated regulator HpaA which binds to and inactivates HpaB during the assembly of the T3S system. In the present study, we show that translocation of HpaA depends on the T3S substrate specificity switch protein HpaC and likely occurs after pilus and translocon assembly. Translocation of HpaA requires the presence of a translocation motif (TrM) in the N-terminal region. The TrM consists of an arginine-and proline-rich amino acid sequence and is also essential for the in vivo function of HpaA. Mutation of the TrM allowed the translocation of HpaA in hpaB mutant strains but not in the wild-type strain, suggesting that the recognition of the TrM depends on HpaB. Strikingly, the contribution of HpaB to the TrM-dependent translocation of HpaA was independent of the presence of the C-terminal HpaB-binding site in HpaA. We propose that HpaB generates a recognition site for the TrM at the T3S system and thus restricts the access to the secretion channel to effector proteins. Possible docking sites for HpaA at the T3S system were identified by in vivo and in vitro interaction studies and include the ATPase HrcN and components of the predicted cytoplasmic sorting platform of the T3S system. Notably, the TrM interfered with the efficient interaction of HpaA with several T3S system components, suggesting that it prevents premature binding of HpaA. Taken together, our data highlight a yet unknown contribution of the TrM and HpaB to substrate recognition and suggest that the TrM increases the binding specificity between HpaA and T3S system components.

Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work.

Selenium Combined with Methyl Jasmonate to Control Tomato Gray Mold by Optimizing Microbial Community Structure in Plants.

Tomato cultivation is seriously affected by infection from Botrytis cinerea. The safe and effective control of tomato gray mold remains elusive. Plant-related microbial communities regulate not only plant metabolism but also plant immune systems. In this study, we observed that Selenium application in soil combined with foliar spraying of methyl jasmonate could reduce Botrytis cinerea infection in tomato fruits and leaves and improve tomato fruit quality. The infection rate of leaves decreased from 42.19% to 25.00%, and the vitamin C content increased by 22.14%. The bacterial community structure of the tomato was studied by using amplicon sequencing technology. The leaf bacterial alpha diversity of tomatoes treated with Se plus methyl jasmonate was significantly higher than that of the control. Then we isolated five strains antagonistic to Botrytis cinerea in vitro from tomato leaves in the treatment of Se plus methyl jasmonate. The antagonistic strains were identified as Bacillus subtilis and Bacillus velezensis. Spraying mixed antagonistic strain suspension significantly inhibited the diameter of Botrytis cinerea with an inhibition rate of 40.99%. This study revealed the key role of plant-beneficial bacteria recruited by Se combined with methyl jasmonate in improving tomato plant disease resistance. These findings may benefit our understanding of the new regulation of microorganisms on Botrytis cinerea.

Penerapan Certainty Factor pada Diagnosis Penyakit Tanaman Tomat

Tomato (Solanum lycopersicum) is a horticultural crop that has important economic value as a vegetable or fruit. Many problems arise in tomato production, causing a decrease in production yields, namely pests and diseases. Handling tomato disease is often constrained due to delays in diagnosing the disease. To overcome this problem, a system was designed that was used to diagnose tomato plant diseases by applying the Certainty Factor (CF) method. The CF method is a method used to express belief in an event in the form of facts or hypotheses based on evidence or expertise. The results obtained is an expert system application consisting of 7 tomato diseases with 16 symptoms. In this system, the user can choose the symptoms on plant, each selected symptom has a weight that will be used to obtain the results of disease determination. Testing on 25 data shows the suitability between the application and the expert is 88%. This represents the system is able to diagnose tomato plant diseases.

Using Artificial Intelligence for Plant Disease Classification Based on Convolutional Neural Network

The plant disease prediction is useful in increasing agricultural production. The plant disease diagnosis through deep learning is a branch of artificial intelligence (AI). It is very important to diagnose plant diseases to improve productivity in agriculture. The plant leaf disease analysis provides us a problem of plant leaf lowest accuracy. The plant for identifying plant disease is to prevent the yield loss of agricultural products. We have proposed a model to validate the data set of the various plants through image processing. The AI-based plant disease detection is very important for sustainable agricultural development. The diseased plant needs to be monitored manually. It requires a lot of work and too much expertise is needed to cope with the plant’s disease in time. The proposed system Convolution Neural Network image processing is to predict the tomato plant diseases. Preprocessing is done in plant leaf image dataset image using Gaussian filter, and then data cleaning, data reduction, and classification are done in disease detection. This describes a method for predicting the disease of plants using the image of their leaves. Further, the algorithm used for the detection of plant diseases and final result of classification in plant disease prediction is described. This demonstrates the technical feasibility of CNN for plant disease classification and provides the accurate result for AI solutions.

First Report of bacterial speck caused by Pseudomonas syringae pv. tomato Race 1 affecting tomato in different Regions of Chile.

In Chile, tomato is one of the most widely cultivated vegetables, with around 5,000 ha for fresh market and 8,000 ha for processing industry. During recent years, symptoms of bacterial speck caused by Pseudomonas syringae pv. tomato, have been observed more frequently in tomato plants in different regions of Chile. This pathogen was first identified in Chile in 1987 (Latorre & Lolas, 1988) and the presence of an apparent new variant was reported in 2004 (Besoain et al. 2004). To characterize the pathogen that was affecting this crop, samples of diseased tomato plants were taken in three regions of Chile. The samples were collected in 2016 in Northern Chile in Lluta Valley from the Arica y Parinacota Region, and in Central Chile, in 2014 in Limache from Valparaíso Region and in 2015 in Pichidegua from O´Higgins Region. Affected tomato plants exhibited dark brown to black lesions surrounded by yellow halos in the leaves, and dark brown to black lesions in the stems, pedicels, and peduncles. Plants tissues were macerated, and the suspension was spread on King's B medium, resulting in fluorescent colonies visualized under 366 nm UV light. LOPAT tests results of three selected isolates from different Regions, were: levan production (+), oxidase reaction (-), potato soft rot (-), arginine dihydrolase production (-), and tobacco hypersensitivity (+) (Lelliot et al. 1966). Molecular identification was carried out by amplification and sequence analysis of housekeeping genes cts, encoding citrate synthase, gyrB, encoding DNA gyrase B, and rpoD, encoding sigma factor 70 (Hwang et al. 2005; Sarkar & Guttmann 2004) (GenBank Accessions No. OK001658-OK001666). BLAST analysis of cts and rpoD genes of the three isolates resulted in a match with a 100% identity (919 bp and 491 bp respectively) with Pseudomonas syringae pv. tomato strain B13-200 (GenBank: CP019871.1). BLAST analysis of gyrB gene of two isolates resulted in a match with a 100% identity (684 bp) and one isolate with 99.85% (683 bp) with Pseudomonas syringae pv. tomato strain B13-200. To identify the race 1, each strain was inoculated in five tomato plants cv. San Pedro, susceptible to both races of P. syringae pv. tomato, and cv. Rio Grande, resistant to race 0. The tomato plants were slightly wounded with a metal sponge and then sprayed with the bacterial suspension (108 CFU mL-1) of each isolate, including the reference strain DC3000 (race 0). Negative controls were sprayed with water. The plants inoculated with Chilean strains in both cv. San Pedro and cv. Rio Grande, showed symptoms of bacterial speck after 7 days. Plants inoculated with DC3000 strain showed symptoms only in cv. San Pedro, whereas control plants remained asymptomatic. Strains were re-isolated from symptomatic plants and identified by gene sequence analyses as Pseudomonas syryngae pv. tomato. This is the first report of Pseudomonas syryngae pv. tomato race 1 in Chile. Race 1 was previously reported in Canada (Lawton and MacNeill. 1986), in Italy (Buonaurio et al. 1996), in California (Arredondo and Davis 2000), in Portugal (Cruz et al. 2010), and in other states in the USA and countries in South America, Europe, Africa, and Australia, becoming the most commonly isolated race today (Cai et al 2011). These results will be the base for future studies of epidemiology, characterization, and virulence in order to explain the outbreak of this disease and the severity of symptoms observed.

Literature Survey of Plant Disease Detection using CNN

Abstract: Modern farming practises have the potential to feed the world's 7.6 billion inhabitants. Despite the availability of sufficient food, individuals continue to suffer from malnutrition. Plant diseases reduce both the amount and the quality of total production. Building an image processing model for prediction or classification applications presents several obstacles. We present a deep learning model for illness detection that makes use of CNN and Capsule Network (CapsNet). The backbone of image processing is a convolutional neural network (CNN). The new architecture called as CapsNet is suggested to reduce the limitations and to achieve greater performance than standard neural networks. In this project, we analyze CNN and CapsNet for tomato plant disease datasets. The performance of both the models are measured and analysed. As a result, this approach may be used to a variety of plants and on a huge scale. Keywords: Leaf disease detection; Convolutional Neural Network (CNN); Capsule Network (CapsNet); Performance Evaluation

An image based prediction system for early blight disease in tomato plants using deep learning algorithm

The global economic system directly depends on the agricultural and farming industry. However, plant diseases are a peril to crops that deteriorates the quality as well as quantity of agricultural produce which pose huge loss to the economy. Therefore, early detection of diseases is crucial to prevent the wrecking of crops. The main aim of this study is to detect the early blight disease in tomato plants beforehand using a prominent deep learning approach, i.e., Convolutional Neural Network (CNN). The current study uses an image-based Tomato Early Blight Disease (TEBD) dataset to develop a disease prediction model. Various image processing techniques, i.e., Background Removal, Augmentation, Resizing, Noise Removal, and Segmentation were applied to obtain a refined dataset. Further, the modified TEBD dataset was trained using the CNN approach to develop an image-based TEBD prediction model. The impact of hyperparameters such as epochs and mini-batch size were also analyzed for different train-test ratios. Afterwards, the performance of the proposed model was precisely evaluated using various performance metrics, i.e., Accuracy, F1-Score, Test loss, Precision, and Recall. The mean value was calculated of different performance metrics for all nine splitting ratios. The best mean accuracy achieved was 98.10% having batch-size 64 and 15 epochs. This model can be used by farmers to reduce the workload and thus will become feasible for them in early treatment and curing of disease. In this way, the proposed approach will prevent the plants from getting severely affected in the early stage.

Plant Health Status Affects the Functional Diversity of the Rhizosphere Microbiome Associated With Solanum lycopersicum

The microorganisms inhabiting soil perform unique functions in the growth and development of plants. However, little is known about how plant health status affects their potential functions. We examined the functional diversity of the microbiome inhabiting the rhizosphere of powdery mildew diseased and healthy tomato plants alongside the bulk soils in South Africa's Northwest Province employing a shotgun metagenomics approach. We envisaged that the functional categories would be abundant in the healthy rhizosphere (HR) of the tomato plant. We collected soil from the rhizosphere of healthy, powdery mildew diseased tomato plants (DR), and bulk soil (BR). After that, their DNA was extracted. The extracted DNA was subjected to shotgun metagenomic sequencing. Our result using the SEED subsystem revealed that a total of fifteen (15) functional categories dominated the healthy rhizosphere, seven (7) functional categories dominated the diseased rhizosphere. At the same time, six (6) functions dominated the bulk soil. Alpha (α) diversity assessment did not reveal a significant difference (p > 0.05) in all the soil samples, but a considerable difference was observed for beta (β) diversity (P = 0.01). The functional categories obtained in this research were highly abundant in HR. Therefore, this study shows that the functions groups of the rhizosphere microbiomes were more abundant in HR samples as compared to others. The high prevalence of functions groups associated with rhizobiomes in the tomato rhizosphere indicates the need for more research to establish the functional genes associated with these rhizosphere microbiomes.

Prediction of Tomato Plant Disease with Meteorological Condition and Artificial Intelligence

Predicting disease in advance plays a vital role in taking preventive measures towards plant protection. The work proposes predicting tomato plant disease with meteorological conditions and artificial intelligence. Support Vector Regression (SVR) and Random Forest Regression (RFR) models are employed for the time-series forecasting of temperature and relative humidity. The models are optimized with GridSearchCV to fine-tune the hyperparameters to better forecast weather parameters. The RMSE for temperature is 0.0369; relative humidity is 1.8442 for the SVR model, 0.0246 for temperature, and 0.5918 for relative humidity with the RFR model. The R2 values show a good prediction performance. The correlation between actual and predicted test values is positive.

Efficacy of Trichoderma harzianum against Fusarium oxysporum and Rhizoctonia solani on bean and tomato plants

Experiments were conducted in the laboratory, greenhouse, and field at the Papua New Guinea University of Technology (PNGUT) to assess the efficacy of Trichoderma harzianum against Rhizoctonia solani and Fusarium oxysporum. The dual culture of T. harzianum with R. solani and F. oxysporum isolated from the diseased bean and tomato plants under laboratory conditions showed 60.1% and 63.3%, and 54.9% and 61.6% growth reduction for R. solani and F. oxysporum, respectively. In greenhouse fungal inoculation experiments, bean and tomato plants showed relative germination index ranging from 0.56 to 1, 0.83 to 1, and disease reduction ranging from 64.8 to 96.1, and 20.3 to 83.7%, respectively. Field experiments involved tests with T. harzianum against one pathogenic fungus or against a combination of both R. solani and F. oxysporum, applied simultaneously as the pathogenic fungi or five days before application of pathogenic fungi. The results for bean and tomato plants showed relative germination index ranging from 0.42 to 0.94, and 0.63 to 0.94, and disease reduction recorded at 63.8 to 96.1%, and 11.3 to 63.9%, respectively. The outcomes of this study will form the basis for further investigation into the potential routine use of Trichoderma spp. as biological control agents against soil-borne pathogens in PNG.

In Vitro and In Vivo Biocontrol of Tomato Fusarium Wilt by Extracts from Brown, Red, and Green Macroalgae

Fusarium wilt caused by Fusarium oxysporum f. sp. lycopersici (FOL) in tomatoes is globally recognized as one of the most significant tomato diseases, both in fields and in greenhouses. Macroalgae contain a diversity of bioactive complexes. This research was carried out to assess the value of the extracts from three macroalgae (Sargassum dentifolium belongs to Phaeophyta, Gracilaria compressa belongs to Rhodophyta, and Ulva lactuca belongs to Chlorophyta) against wilt disease in tomato plants. The fungal pathogen was isolated from diseased tomato plants growing in several parts of Saudi Arabia. Isolates of the pathogen were identified by morphological and molecular methods. Three organic solvents, in addition to water, were used for extraction to assess the effect of reducing FOL hyphal growth on potato dextrose agar (PDA). Radial reductions in pathogen hyphal growth were seen with all of the solvent and water extracts, but the three macroalgae methanol extracts that were tested showed the greatest reduction in pathogen hyphal growth. The total phenol content of the S. dentifolium extract was higher than that of the other two macroalgae. The phenolic compounds showed variability in all of the extracts that were identified and calculated by high-performance liquid chromatography (HPLC). Phloroglucinol (7.34 mg/g dry weight), vanillic acid (7.28 mg/g dry weight), and gallic acid (6.89 mg/g dry weight) were the phenolic compounds with the highest concentrations in the S. dentifolium, G. compressa, and U. lactuca extracts, respectively. The mycelium of FOL treated with a crude macroalgae extract of tested at 100 µg/mL was examined with a scanning electron microscope. The results showed an obvious difference between the extract-treated and untreated hyphae. The extract-treated hyphae collapsed and bruised, as well as; empty and dead. In the greenhouse experiment, S. dentifolium powder was used to evaluate its effect on disease decline. It led to a decrease in disease severity of 40.8%. The highest total yield (560.8 g) was obtained from the plants treated with S. dentifolium powder. We recommend the use of macroalgae extracts to combat fungal phytopathogens. Because chemical fungicides are extremely toxic to humans and the environment, macroalgae extracts are a good alternative that can be widely and safely used in the field.

Effects of Solvent Extracted Bioactive Compounds from the Bark, Roots and Leaves of Croton jatrophoides on Tomato Wilt Disease (Fusarium. oxysporum f. sp. lycopersici)

Fusarium wilt of tomatoes, caused by Fusarium oxysporum, is a major and most prevalent soil borne disease in tomato plants both in the field and green house. The economic impact of this disease cannot be underestimated. All recommended control strategies have proved to be ineffective in controlling the pathogen. Studies have shown that Croton jatrophoides has medicinal activity against the phytopathogens. This study was conducted to evaluate the efficacy of extracts from Croton jatrophoides to suppress Fusarium oxysporum. Specific objectives were to extract bioactive compounds from C. jatrophoides for use against F. oxysporum; to determine the minimum inhibition concentration of crude extract that could elucidate response against F. oxysporum; to test in-vitro the efficacy and levels of bioactive compounds extracted using selected solvents from bark, the leaves and the root; to determine interaction effects between the treatments and the crude extracts. The experiment involved the isolation of bioactive crude extracts from the roots, bark and leaves of Croton jatrophoides plant using three solvents, namely hexane (non-polar), ethyl acetate (moderately polar) and methanol (highly polar). The isolated crude extracts from each solvent were concentrated using rotary evaporator, then purified using micro-filters. These extracts were then used for testing their antifungal activity in Potatoes Dextrose Agar (PDA) petri-plates containing Fusarium oxysporum inoculum. The results indicated that the quantity of the solvent required for exhaustive extraction of bioactive compounds from the ground material was at 180 mL. The minimum inhibition concentration that elucidated response against F. oxysporum was found to be 50.0 mg/mL. Non-polar solvents such as hexane and ethyl acetate were found to have the highest abilities in extracting bioactive compounds from C. jatropoides materials since most of these compounds that had antifungal activity were non-polar. Hexane derived extracts had the highest significant efficacy in control of Fusarioum oxysporum, comparable to Rindomil, a positive control. Dimethyl sulfoxide (DMSO) had the least effect on Fusarioum oxysporum. In the absence of hexane, ethyl acetate was the second most suited solvent for extraction of bioactive compounds from C. jatrophoides. Methanol had the least abilities in extraction of bioactive compounds from C. jatrophoides. There were interaction effects between the parts of the plants from which bioactive compounds were derived and the type of solvents used.

Alternative Control of Tomato Wilt Using the Aqueous Extract of Calotropis procera

Calotropis procera (Aiton) Dryand (commonly known as the apple of sodom, calotrope, and giant milkweed) is an evergreen, perennial shrub of the Apocynaceae family, and is mainly found in arid and semi-arid regions. Previous studies have established the toxic effects of Calotropis procera (C. procera) (Aiton). Its extract is used as one of the vital alternatives to pesticides due to its effective impacts on several pathogens and to combat insect pests that cause severe damage to several crops, but so far, its effects on combating Fusarium oxysporum f. sp. lycopersici (FOL) have not yet been evaluated. Therefore, the current study focuses on evaluating the effects of the aqueous extract of this plant on FOL by studying physicochemical indicators. In Saudi Arabia and many other countries, such disease is considered as one of the critical factors affecting tomato growth and production. For the control of Fusarium wilt disease (Fwd), many strategies have been developed, but are limited in their use. Ten isolates of the pathogens were obtained from diseased tomato plants. These isolates were able to exhibit symptoms of tomato wilt, and they varied in their virulence. Isolate No. 5 caused the highest severity of the disease (73%). According to these results, we used this isolate in the following experiment, which was identified by ITS sequences as F. oxysporum f. sp. lycopersici. Following intensive screening studies, we selected aqueous plant extract of C. procera to study its effectiveness against Fwd of tomato plants and their antifungal activity or induce systemic resistance in plants. All concentrations of C. procera extracts suppressed growth of the pathogen. The highest reduction of mycelia growth was obtained by aqueous extract at 15%, it reduced the pathogen growth to 70.2%, relative to the control. In greenhouse conditions, the aqueous C. procera extract at 5, 10, and 15% significantly reduced Fwd of the tomato compared to the infected control (52.01, 69.86, and 83.6%, respectively). The use of aqueous C. procera extract at 5, 10, and 15% significantly enhanced the fresh and dry weight of tomato plants (g plant−1) compared to inoculated plants (for fresh weight: 50, 66.6, and 86.6 and for dry weight: 60, 100, and 120%, respectively). Total phenols and flavonoids as well as antioxidant enzymes were increased in inoculated or non-inoculated tomato plants after being treated with aqueous extracts of C. procera.