Sigatoka Disease Of Banana Research Articles

A Deep Learning Model for Classifying Black Sigatoka Disease in Banana Leaves Based on Infection Stages

Objective: This research study aims to develop an efficient deep-learning model to detect and classify stages of Black Sigatoka disease in banana plants. Methods: In this study, deep learning techniques, specifically the basic Convolutional Neural Network (CNN) and VGG16 models, were used to address the challenge of identifying Black Sigatoka disease in banana leaves early on. The tests were conducted on a dataset containing labelled images of banana leaves, assessing their effectiveness based on criteria such as accuracy, precision, recall, and F1-score after adjusting hyperparameters for optimal outcomes. Findings: The results of the trials revealed that the basic CNN model attained a training accuracy of 96% and a validation accuracy of 89%, surpassing the performance of the VGG16 model. The VGG16 model, on the other hand, had a training accuracy of 92% and a validation accuracy of 89%. Across precision, recall, and F1 score measurements, the basic CNN model consistently outperformed the VGG16 model, with scores averaging 0.90 for all three metrics compared to VGG16's precision of 0.80, recall of 0.75, and F1 score of 0.75. The CNN model demonstrated its efficiency by stopping training at 26 epochs, whereas VGG16 completed training in 21 epochs. This demonstrates its effectiveness in detecting Black Sigatoka while utilising minimal resources. Novelty: A significant component of this study is its emphasis on identifying the stages of Black Sigatoka disease, which is commonly overlooked in research. By studying disease progression, this study provides insights for early intervention and disease management, aiding efforts to lessen the impact of Black Sigatoka on banana farming. Keywords: Black Sigatoka, Deep Learning, Disease Stages, Convolutional Neural Network, Classification, Identification

Major diseases of field and horticultural crops in Northern Bihar region of India

A survey was conducted to determine the status of major diseases of field and horticultural crops grown in Saharsa, Supaul, Madhepura and Khagaria districts of northern Bihar, India. Three blocks in each district and three villages in each block were surveyed through a random field survey method. Per cent disease incidence was recorded on randomly selected plants in a particular field of selected location. The incidence of diseases was observed on the basis of typical field symptoms and later the association was confirmed through microscopic examinations in the laboratory. In view of maximum diseases incidence; foliar blight of wheat, sheath blight of rice, turcicum leaf blight of maize, mungbean yellow mosaic virus, Alternaria blight of mustard were recorded with >50% incidence in these districts. However, dry root rot and wilt of chickpea and lentil, wilt of pigeonpea, powdery mildew of pea, Alternaria leaf spot of linseed, Fusarium wilt and red rot of sugarcane and root rot of jute were noticed with 10 to 50% incidence. In fruit crops; sigatoka disease of banana was noticed with >50% incidence, while, anthracnose/ die back and floral malformation of mango, wilt of banana, foot rot of papaya and anthracnose of guava and citrus canker were observed with 10-50% incidence. Diseases viz., late blight, bacterial wilt, black leaf spot/ rot, leaf curl, yellow vein mosaic virus, die back and late blight were recorded with >50% incidence in tomato, brinjal, cauliflower, cucurbits, okra, chilli and potato, respectively. The purple blotch of onion, black leaf rot of cabbage and collar rot of elephant foot yam were noticed with 10-50% incidences.

Prediction of Black Sigatoka Disease in Banana Plants By Data Mining Classification Techniques using Scikit for Python

Agriculture has been evolving since humans started cultivating plants for food consumption. As the agriculture field evolves, the disease control measures too have evolved. Now in this modern era, disease in plants can be easily identified using computers. Data mining is the process of obtaining the useful information from the data. Before the electronic era, diseases in plants are identified just by seeing the symptoms of the plants. Similarly, we can identify the diseases in plants using data mining by supplying the disease symptoms data and classify them accordingly. The purpose of this paper is focusing on the prediction of the diseases from images of black sigatoka disease and uses the following methods: MultilayerPerceptrons, SVM,KNeighborsClassifier,K-NeighborsRegressor, Gaussian Process Regressor, Gaussian Process Classifier, GaussianNB, Decision Tree Classifier, Decision Tree Regressor, linear models such as Linear Regression, RidgeCV, Lasso, ElasticNet, Logistic RegressionCV, SGD Classifier, Perceptron and Passive Aggressive Classifier and ensemble models of the above classifiers. The results are compared, and multilayer perceptron model is seen to give better results for individual classifiers and ensemble of week classifiers gives better results when ensembled. In future, a new hybrid algorithm would be used from the above algorithms for attaining better accuracy. The scikit is a library used for classification, clustering, regression, dimensionality reduction,model selection and preprocessing. Our paper discusses various classifiers used in scikit-learn library for Python and their ensembling is done. This can be applied to all the classification tasks. Classification is done for classifying the black sigatoka disease in banana from healthy leaves.This disease is the most vulnerable one among banana plants.

Genetic variability of Pseudocercospora fijiensis, the black Sigatoka pathogen of banana (Musa spp.) in Mexico

Pseudocercospora (previously known as Mycosphaerella) fijiensis causes black Sigatoka disease in banana (Musa spp.) and is considered to be the most devastating pathogen of this crop worldwide. To improve knowledge of its evolutionary patterns, this study determined the genetic variability of populations from two regions of Mexico: Central Pacific (Colima and Michoacan) and Southern (Chiapas, Tabasco and Oaxaca), using 10 simple sequence repeat (SSR) loci and the MAT‐specific PCR assay. Both mating types were present in all regions under study, with frequencies of 63% MAT1‐1 and 37% MAT1‐2. The SSR markers showed an average of three alleles per locus, resulting in 34 alleles in total. The genetic diversity (HT) was 0.3308, but at the local level (HS) ranged from 0.0976 (Colima) to 0.2228 (Oaxaca). However, the genotypic diversity was usually high (H′ > 2.4, S > 0.89). Cluster analysis grouped the isolates into five clusters with high statistical support (au > 80%), suggesting a geographic organization of the genetic variability of P. fijiensis; AMOVA, the minimum spanning tree and the population structure analysis supported this result, and all data indicated that the major genetic differences were between the different populations under analysis. Thus, the high level of genetic variability in P. fijiensis is attributed partly to a high rate of sexual reproduction, and also to a strong evolutionary capacity coupled with isolation due to limited genetic flow between distant populations. Both possibilities could be playing a relevant role in population differentiation of the pathogen.

First report of black Sigatoka disease in banana caused by Mycosphaerella fijiensis on Reunion Island

Pseudocercospora fijiensis, the causal agent of black Sigatoka disease of banana (Musa spp.) (BLSD), is considered to be the major economic threat for banana cultivated for export (de Lapeyre de Bellaire et al., 2010). The disease has a …

Prevalence of Sigatoka disease of banana in Maharashtra

Prevalence of Sigatoka disease of banana in Maharashtra

Agrobacterium tumefaciens-Mediated Transformation of Pseudocercospora fijiensis to Determine the Role of PfHog1 in Osmotic Stress Regulation and Virulence Modulation.

Black Sigatoka disease, caused by Pseudocercospora fijiensis is a serious constraint to banana production worldwide. The disease continues to spread in new ecological niches and there is an urgent need to develop strategies for its control. The high osmolarity glycerol (HOG) pathway in Saccharomyces cerevisiae is well known to respond to changes in external osmolarity. HOG pathway activation leads to phosphorylation, activation and nuclear transduction of the HOG1 mitogen-activated protein kinases (MAPKs). The activated HOG1 triggers several responses to osmotic stress, including up or down regulation of different genes, regulation of protein translation, adjustments to cell cycle progression and synthesis of osmolyte glycerol. This study investigated the role of the MAPK-encoding PfHog1 gene on osmotic stress adaptation and virulence of P. fijiensis. RNA interference-mediated gene silencing of PfHog1 significantly suppressed growth of P. fijiensis on potato dextrose agar media supplemented with 1 M NaCl, indicating that PfHog1 regulates osmotic stress. In addition, virulence of the PfHog1-silenced mutants of P. fijiensis on banana was significantly reduced, as observed from the low rates of necrosis and disease development on the infected leaves. Staining with lacto phenol cotton blue further confirmed the impaired mycelial growth of the PfHog1 in the infected leaf tissues, which was further confirmed with quantification of the fungal biomass using absolute- quantitative PCR. Collectively, these findings demonstrate that PfHog1 plays a critical role in osmotic stress regulation and virulence of P. fijiensis on its host banana. Thus, PfHog1 could be an interesting target for the control of black Sigatoka disease in banana.

Cloning, in silico structural characterization and expression analysis of MfAtr4, an ABC transporter from the banana pathogen Mycosphaerella fijiensis

ABC transporters are membrane proteins that use the energy released from the hydrolysis of ATP to drive the transport of compounds across biological membranes. In some plants, pathogenic fungi ABC transporters play a role as virulence factors by mediating the export of plant defense compounds or fungal virulence factors. Mycosphaerella fijiensis, the causal agent of black Sigatoka disease in banana, is the main constraint for the banana industry worldwide. So far, little is known about molecular mechanism that it uses to infect the host. In this study, degenerated primers designed from fungal ABC transporters known to be involved in virulence were used to isolate homologs from M. fijiensis. Here, we reported the full cloning of MfAtr4 a putative ortholog of MgAtr4 , an ABC transporter of the related Mycosphaerella graminicola with a function in virulence. Similarities and differences with its presumed ortholog MgAtr4 are described, and the putative function of MfAtr4 are discussed. Analysis of MfAtr4 gene expression in field banana samples exhibiting visible symptoms of black Sigatoka disease indicated a higher expression of MfAtr4 during the first symptomatic stages in comparison to the late necrotrophic phases, suggesting a role for MfAtr4 in the early stages of pathogenic development of M. fijiensis . Key words : ABC transporters, virulence factors, MgAtr4 ortholog, Mycosphaerella fijiensis, black Sigatoka, Musa sp.

Physiological effects of the hydrophilic phytotoxins produced by Mycosphaerella fijiensis, the causal agent of black sigatoka in banana plants

Although Mycosphaerella fijiensis, the causal agent of black sigatoka disease of banana, has been known to produce numerous lipophilic host-selective (HSTs) and nonhost selective phytotoxins (non-HSTs), only recently we have reported that the pathogen also produces hydrophilic phytotoxins. Here we examined the effect of light on the toxicity of the hydrophilic phytotoxins and estimated the electrolyte leakage and H2O2 and superoxide generation in detached banana leaves to study their mode of action at the cellular level. Nonhost plant species were also tested to determine whether the toxins are HSTs or non-HSTs. Our results suggest that the hydrophilic phytotoxins are non-HSTs, that their phytotoxicity is not light dependent, and that they may act at the plasma membrane by altering permeability through oxidative damage, by inducing ROS production as part of their mechanism of action.

Fourteen polymorphic microsatellite markers for the fungal banana pathogen Mycosphaerella fijiensis

Fourteen polymorphic microsatellite markers were developed for Mycosphaerella fijiensis, a fungus causing the black sigatoka disease in banana. The sequenced genome of M. fijiensis was screened for sequences with single sequence repeats (SSRs) using a Perl script. Fourteen SSR loci, evaluated on 48 M. fijiensis isolates from Hawaii, were identified to be highly polymorphic. These markers revealed two to 19 alleles, with an average of 6.43 alleles per locus. The estimated gene diversity ranged from 0.091 to 0.930 across the 14 microsatellite loci. The SSR markers developed would be useful for population genetics studies of M. fijiensis.

The Genetic Structure of Australian Populations of Mycosphaerella musicola Suggests Restricted Gene Flow at the Continental Scale

ABSTRACT Mycosphaerella musicola causes Sigatoka disease of banana and is endemic to Australia. The population genetic structure of M. musicola in Australia was examined by applying single-copy restriction fragment length polymorphism probes to hierarchically sampled populations collected along the Australian east coast. The 363 isolates studied were from 16 plantations at 12 sites in four different regions, and comprised 11 populations. These populations displayed moderate levels of gene diversity (H = 0.142 to 0.369) and similar levels of genotypic richness and evenness. Populations were dominated by unique genotypes, but isolates sharing the same genotype (putative clones) were detected. Genotype distribution was highly localized within each population, and the majority of putative clones were detected for isolates sampled from different sporodochia in the same lesion or different lesions on a plant. Multilocus gametic disequilibrium tests provided further evidence of a degree of clonality within the populations at the plant scale. A complex pattern of population differentiation was detected for M. musicola in Australia. Populations sampled from plantations outside the two major production areas were genetically very different to all other populations. Differentiation was much lower between populations of the two major production areas, despite their geographic separation of over 1,000 km. These results suggest low gene flow at the continental scale due to limited spore dispersal and the movement of infected plant material.

Identification of the pathogen associated with Sigatoka disease of banana in South Africa

Three species of Mycosphaerella are known to cause Sigatoka leaf diseases in banana. M. musicola causes yellow Sigatoka, M. fijiensis causes black Sigatoka and M. eumusae causes eumusae leaf spot. Linie is known about the presence of Sigatoka leaf diseases of banana in South Africa. Although yellow Sigatoka has previously been reported, the identity of the causal organism has never been confirmed. Extensive surveys of the five banana-growing areas in South Africa were conducted between 1999 and 2001, which resulted in the collection of various Sigatoka-like leaf spot samples. After morphological examination of the infected material, monoconidial isolates of the causal organism were established from each sample. A molecular confirmation of identity was conducted using species-specific primers for M. musicola and M. fijiensis. Sequence data of the ITS region was further obtained to compare the South African Mycosphaerella isolates from banana with M. musicola, M. fijiensis and M. eumusae. These results confirm that only M. musicola is presently associated with yellow Sigatoka symptoms in South Africa.

The influence of exposure to ultraviolet radiation in simulated sunlight on ascospores causing Black Sigatoka disease of banana and plantain

The influence of ultraviolet (UV) radiation in simulated natural sunlight on the viability of ascospores of Mycosphaerella fijiensis, the cause of Black Sigatoka disease in banana and plantain, has been investigated as part of a study to assess the windborne spread of this pathogen from mainland Central and South America into the Caribbean. Spores were killed following continuous exposure to UV radiation for periods of 6 h or over. This relatively short exposure time suggests that the distances over which viable spores can be transported will be determined not only by the speed of the wind but also the amount of cloud cover and the time off day that spore release occurs. On this basis, wind dispersal of viable spores over distances greater than a few hundred kilometres is unlikely. These conclusions are reinforced by an examination of historical reports of the arrival of the disease in previously uninfected areas of the Americas and Africa.

Airborne spore loads and mesoscale dispersal of the fungal pathogens causing Sigatoka diseases in banana and plantain

The mesoscale dispersal of ascospores and conidia of the fungal pathogenMycosphaerella fijiensis, causing Black Sigatoka disease in banana and plantain, was investigated under field conditions in Costa Rica. Spores were sampled daily during the period April–August 1995, using three volumetric spore traps positioned in a 5 km straight line across the floor and up one side of a valley, to an elevation of 1000 m. The number of spores in each trap suggested that spores were not being released from nearby sources, but that they may have been transported from inoculum sources up to 40 km away. The implications of this when assessing the longer-distance transport of viable spores to new infection sites are discussed.

Establishment of Sigatoka Disease of Bananas in the Greenhouse

ALTHOUGH Sigatoka disease of banana has been known for a half-century and is a serious problem, in most banana-growing areas, many fundamental aspects of the disease and of the life-history of the causal organism, Mycosphaerella musicola Leach (= Cercospora musae Zimm.), remain poorly understood. Present knowledge has been derived largely from field observation of the disease and pathogen and from field inoculation studies1–4. Successful greenhouse inoculations have not been reported, although many investigators have been interested in studying the disease under controlled conditions. In the field, Sigatoka outbreaks are coincident with periods of wet, humid weather1,2, and laboratory investigations have shown that conidia of C. musae must be immersed in a water film for germination to occur2. Failure to obtain adequate humidity and/or moisture films on the leaf surface may account for the lack of reported greenhouse studies of this disease.