Sigatoka Disease Research Articles

The Effect of Some Biological Fungicides and Liquid Organic Fertilizers on Sigatoka Disease on ‘Mas’ Banana Plants (Musa acuminata Colla)

Banana (Musa acuminata Colla) is a tropical fruit with great economic potential and export prospects. According to the Directorate General of Horticulture, Ministry of Agriculture, banana production from 2009 to 2014 fluctuated due to attacks by Plant Destruction Organisms (PDO), particularly Sigatoka disease caused by the fungus Mycosphaerella musciola. This study investigated biological agents like Trichoderma sp. and Gliocladium sp., along with liquid organic fertilizers, to control the disease without synthetic fungicides.The research, conducted at the University of Muhammadiyah Malang, used a split-plot design with two factors: biological pesticides (Tricogreen, Natural Glio, Trico-G) and liquid organic fertilizers (rabbit urine POC, shrimp shell MOL, banana hump MOL). The experiment had nine treatment combinations with three plant samples each. Results showed that biological pesticides significantly reduced Sigatoka disease intensity, with the J3P3 treatment (Trico-G and Banana Hump MOL) achieving the lowest infection rate of 29.55%, reducing disease intensity by 70.45%. Liquid organic fertilizers also improved plant growth, increasing leaf number and leaf area. This indicates that biological agents and organic fertilizers effectively manage Sigatoka disease and promote healthy banana plant growth.

Diversity of Improved Diploids and Commercial Triploids from Musa spp. via Molecular Markers

Banana breeding consists of obtaining diploid, triploid, and tetraploid intra- and interspecific hybrids by conventional breeding methods with the objective of aggregating characteristics of agronomic and commercial interest. Given the narrow genetic base of bananas, Embrapa’s Banana Genetic Breeding Program (BGBP) aims at crosses between improved diploids (ID) (ID × ID) and between improved diploids (ID) and commercial triploids (ID × CTP) and tetraploids (ID × CTT), in order to increase the genetic base and variability in bananas regarding agronomic traits of interest and resistance to main biotic and abiotic factors. These improved diploids are resistant to main fungal diseases such as yellow (YSD) and black Sigatoka (BSD) disease and Fusarium wilt (race 1 and subtropical race 4), the latter being one of the most devastating diseases in bananas. The genetic diversity between 22 improved diploids and seven commercial banana triploids was analyzed using DNA molecular markers. Five IRAP (Inter-Retrotransposon Amplified Polymorphism, 7 ISSR (Inter-Simple Sequence Repeats) and 12 SSR (Simple Sequence Repeat) markers were used. The genetic dissimilarity matrix was based on the Jaccard dissimilarity index; clusters were separated using the UPGMA (Unweighted Pair Group Method With Arithmetic Mean) method and cophenetic correlation of 0.8755. This study of the genetic diversity between improved diploids and commercial triploids, based on the genetic dissimilarity matrix, revealed that the most dissimilar diploids were DM23 and DM15 (74%) and DM16 and DM15 (74%). The smallest genetic distances between the improved diploids and commercial triploids were between TCGN25 and DM17 (50%) and TCN26 and DM17 (50%). The genetic distance matrix also revealed important genotypes to be used in crosses in order to maintain good characteristics in commercial triploids when crossed with improved diploids. The results of our study provide better breeding strategies for one of the largest banana-breeding programs worldwide focused on the development of banana varieties resistant to main biotic and abiotic factors.

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

Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques

This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets.

Unveiling the Full Protein Effectorome of the Black Sigatoka Pathogen Pseudocercospora fijiensis—An In Silico Approach

Pseudocercospora (previously Mycosphaerella) fijiensis is a hemibiotroph fungus and the causal agent of black Sigatoka disease, one of the most significant threats to banana production worldwide. Only a few genomics reports have paid any attention to effector proteins, which are key players in pathogenicity. These reports focus on canonical effectors: small secreted proteins, rich in cysteines, containing a signal peptide and no transmembrane domain. Thus, bias in previous reports has resulted in the non-canonical effectors being, in effect, excluded from the discussion of effectors in P. fijiensis pathogenicity. Here, using WideEffHunter and EffHunter, bioinformatic tools which identify non-canonical and canonical effectors, respectively, we predict, for the first time, the full effectorome of P. fijiensis. This complete effectorome comprises 5179 proteins: 240 canonical and 4939 non-canonical effectors. Protein families related to key functions of the hemibiotrophic lifestyle, such as Salicylate hydroxylase and Isochorismatase, are widely represented families of effectors in the P. fijiensis genome. An analysis of the gene distribution in core and dispensable scaffolds of both classes of effectors revealed a novel genomic structure of the effectorome. The majority of the effectors (canonical and non-canonical) were found to be harbored in the core scaffolds, while dispensable scaffolds harbored less than 10% of the effectors, all of which were non-canonical. Additionally, we found the motifs RXLR, YFWxC, LysM, EAR, [Li]xAR, PDI, CRN, and ToxA in the effectors of P. fijiensis. This novel genomic structure of effectors (more enriched in the core than in the dispensable genome), as well as the occurrence of effector motifs which were also observed in four other fungi, evidences that these phenomena are not unique to P. fijiensis; rather, they are widely occurring characteristics of effectors in other fungi.

Optimizing Pathogen Control through Mixed Cocoa–Plantain Agroecosystems in the Ecuadorian Coastal Region

Mixed production systems play a vital role in the economic sustainability and ecological balance of agroecosystems. Cocoa and plantain are key crops in Ecuador but face phytosanitary challenges, like witches’ broom and black sigatoka diseases, especially when cultivated under monocropping systems. Combining habitat manipulation with adaptive pathogen management (APM) strategies can mitigate these challenges, but their efficacy in mixed cropping systems remains unclear. This study investigates disease and pest incidence in mixed cocoa–plantain systems during the establishment phase, focusing on the impact of spatial arrangements. Mixed agroecosystems showed a lower witches’ broom incidence in cocoa than monocultures. Whereas, in plantain, there was a consistent black sigatoka incidence across spatial arrangements but a lower infection rate per leaf within mixed systems. We found varied nematode populations with monocultures hosting the highest root damage due to phytoparasitic nematodes. Weevil populations were also influenced by spatial arrangements with monocultures among the highest. Overall, mixed agroecosystems influence disease and pest incidence, potentially hindering pathogen spread. Plantain–cocoa associations reduce disease incidence in cocoa but may not affect the overall incidence of black sigatoka in plantain, at least during the establishment phase. Continued monitoring is crucial for understanding the long-term impacts and microclimatic effects on pest populations that could offer sustainable pest management strategies, reducing the reliance on chemical pesticides.

The Sigatoka Disease Complex Caused by Pseudocercospora spp. and Other Fungal Pathogens Associated with Musa spp. in Puerto Rico.

Bananas (Musa spp.) are among the world's most economically important staple food crops. The most important fungal leaf diseases of Musa spp. worldwide are caused by the Sigatoka disease complex, which comprises black Sigatoka (Pseudocercospora fijiensis), yellow Sigatoka (P. musae), and Eumusae leaf spot (P. eumusae). Considering the rapid spreading rate of black Sigatoka in Puerto Rico since its first observation in 2004, a disease survey was conducted from 2018 to 2020 to evaluate the Sigatoka disease complex on the island. Sixty-one leaf samples showing Sigatoka-like symptoms were collected throughout the island for diagnosis by molecular approaches and fungal isolation. Molecular analysis using species-specific primers for P. fijiensis, P. musae, and P. eumusae detected the presence of P. fijiensis in 50 leaf samples. Thirty-eight fungal isolates were collected and identified by morphology and genomic sequencing from various nuclear genes. The analysis identified 24 isolates as P. fijiensis, while the rest of the isolates belonged to the genus Cladosporium spp. and Cladosporium-like spp. (n = 5), Neocordana musae (n = 2), Zasmidium spp. (n = 6), and Z. musigenum (n = 1). The high frequency of P. fijiensis found in leaf samples and collected isolates suggests that black Sigatoka has displaced the yellow Sigatoka (P. musae) in Puerto Rico. Accurate identification of fungal species causing foliar diseases in Musa spp. will allow the establishment of quarantine regulations and specific management approaches in Puerto Rico.

Control Model of Banana Black Sigatoka Disease with Seasonality

Control Model of Banana Black Sigatoka Disease with Seasonality

Resistance to Site-Specific Succinate Dehydrogenase Inhibitor Fungicides Is Pervasive in Populations of Black and Yellow Sigatoka Pathogens in Banana Plantations from Southeastern Brazil

The Sigatoka disease complex (SDC), caused by Mycosphaerella fijiensis (Mf) and M. musicola (Mm), comprises the most destructive fungal leaf streak and spot diseases of commercial banana crops worldwide. In Brazil, the site-specific succinate dehydrogenase inhibitor (SDHI) fungicides labeled for SDC management since 2014 present a high risk for the emergence of resistance if deployed intensively and solo. Our study determined the levels of sensitivity to boscalid and fluxapyroxad in four populations of the SDC pathogens sampled in 2020 from three distinct geographical regions under contrasting fungicide programs. Resistance, defined as EC50 values exceeding 20 µg mL−1, was prevalent at 59.7% for fluxapyroxad and 94.0% for boscalid. Only 1.5% of isolates exhibited sensitivity to both fungicides. We also assessed the changes in the corresponding fungicide target protein-encoding genes (SdhB, C, and D). None of the target site alterations detected were associated with reduced sensitivity. A second SdhC paralog was also analyzed, but target alterations were not found. However, MDR (multidrug resistance) was detected in a selection of isolates. Further monitoring for Sdh target mutations will be important, but an important role for other resistance mechanisms such as the presence of additional Sdh paralogs and MDR cannot be ruled out. These results highlight the importance of implementing sound anti-resistance management strategies when SDHI fungicides are deployed for the management of SDC.

Pseudocercospora fijiensis mycelia-based infection system enhances investigational efficacy of P. fijiensis-banana pathosystem

Screening procedures for black Sigatoka have limitations. Thus, there is need for alternative screening procedure. A robust controlled-environment methodology for testing reaction of banana genotypes to Pseudocercospora fijiensis is, thus, still required. The objective of this study was, therefore, to assess the effect of P. fijiensis fragmented mycelia-based inoculum on black Sigatoka development in banana under screen house conditions with the view of developing a procedure for early assessment of resistance. Black Sigatoka severity increased significantly (P < 0.05) over time in all genotypes apart from Kayinja. Significant differences (P < 0.05) in severity were also recorded among the genotypes at 3, 4, 5 and 6 weeks after inoculation. All east African highland banana and plantain genotypes tested exhibited susceptible reaction, characterized by rapid progression of symptoms to necrotic lesions. Kayinja and M9 hybrids exhibited resistant reactions, characterized by small necrotic specks and chlorotic or brown blotches, respectively. The fragmented mycelia-based infection system classified the banana genotypes into resistant and susceptible clones, making it a reliable and efficient infection technique to assess black Sigatoka disease damage. The infection system is recommended for early screening for black Sigatoka resistance.

Screening of Triploid Banana Population Under Natural and Controlled Black Sigatoka Disease for Genomic Selection.

Black sigatoka disease (BSD) is the most important foliar threat in banana production, and breeding efforts against it should take advantage of genomic selection (GS), which has become one of the most explored tools to increase genetic gain, save time, and reduce selection costs. To evaluate the potential of GS in banana for BSD, 210 triploid accessions were obtained from the African Banana and Plantain Research Center to constitute a training population. The variability in the population was assessed at the phenotypic level using BSD- and agronomic-related traits and at the molecular level using single-nucleotide polymorphisms (SNPs). The analysis of variance showed a significant difference between accessions for almost all traits measured, although at the genomic group level, there was no significant difference for BSD-related traits. The index of non-spotted leaves among accessions ranged from 0.11 to 0.8. The accessions screening in controlled conditions confirmed the susceptibility of all genomic groups to BSD. The principal components analysis with phenotypic data revealed no clear diversity partition of the population. However, the structure analysis and the hierarchical clustering analysis with SNPs grouped the population into four clusters and two subpopulations, respectively. The field and laboratory screening of the banana GS training population confirmed that all genomic groups are susceptible to BSD but did not reveal any genetic structure, whereas SNP markers exhibited clear genetic structure and provided useful information in the perspective of applying GS.

EFFICACY ANTIMICROBIAL ENDOPHYTICS METABOLITES TO CONTROL SIGATOKA DISEASE (MYCOSPHAERELLA MUSICOLA) ON BANANAS IN NORTHERN SUMATERA

The secondary metabolites show diverse biological activities, including phytotoxic, antibacterial, antifungal, and antitumor. This study aimed to obtain secondary metabolites that are more effective in suppressing the development of Sigatoka disease in bananas. A field trial was performed to screen the antifungal metabolitesactivity of three isolates against Mycosphaerella musicola. This research used a randomised block design with four treatments and six replications. The results showed that the fastest incubation period was obtained at 14 days in the control treatment, and with Bacillus sp occurred after 30 days, 25 days in the treatment of Pseudomonas aeruginosa, and 40 days in the treatment of Trichoderma asperellum. The percentage of disease incidence and spot area on the leaves was most significant in the control treatment, 62,5% and 66,45%, then followed by Pseudomonas aeruginosa respectively, 41,67% and 34,48%, and the lowest disease incidence and spot area in the treatment, Trichodermasp were 25% and 21,78%, respectively. Further, the lowest disease severity was found in the secondary metabolite of Trichoderma asperellum at 23.36%, followed by Pseudomonas aeruginosa secondary metabolite at 32.57%. Meanwhile, the highest disease severity was found in the control treatment, with an average of 60.27%. Secondary metabolites of Bacillus sp, Trichoderma asperellum and Pseudomonas aeruginosa effectively suppress Sigatoka disease. The highest banana production was found in the secondary metabolite of Trichoderma asperellum, with a harvestweight of 13.98 kg, and the lowest harvest weight found in the control treatment was 11.90 kg

Agronomic performance and resistance level of banana cultivars to Mycosphaerella sp. under subtropical conditions

Agronomic performance of banana cultivars varies according to each ecological zone. Sigatoka disease complex causes severe economic losses to banana growers. Climate conditions associated to genotypes of banana plants influence several agronomic traits related to this disease. The objective of this study was to evaluate the agronomic performance of banana cultivars naturally infected with Mycosphaerella sp. under subtropical conditions. Severity of Sigatoka disease complex and agronomic performance of nine cultivars were evaluated during three successive productive cycles. Tetraploid hybrids showed lower cycle from seedling to harvest, disease severity and higher number of leaves at harvest. Pomme and Cavendish subgroups were more susceptible to Sigatoka disease complex and had lower numbers of leaves at the harvest. ‘BRS SCS Belluna’ showed moderate susceptibility to Sigatoka disease complex. ‘FHIA 1’ and ‘Nanicão’ produced the heaviest bunch. Path analysis showed that Sigatoka disease complex can negatively affect bunch weight for two consecutive cropping cycles.

Pseudocercospora fijiensis Conidial Germination Is Dominated by Pathogenicity Factors and Effectors.

Conidia play a vital role in the survival and rapid spread of fungi. Many biological processes of conidia, such as adhesion, signal transduction, the regulation of oxidative stress, and autophagy, have been well studied. In contrast, the contribution of pathogenicity factors during the development of conidia in fungal phytopathogens has been poorly investigated. To date, few reports have centered on the pathogenicity functions of fungal phytopathogen conidia. Pseudocercospora fijiensis is a hemibiotrophic fungus and the causal agent of the black Sigatoka disease in bananas and plantains. Here, a conidial transcriptome of P. fijiensis was characterized computationally. Carbohydrates, amino acids, and lipid metabolisms presented the highest number of annotations in Gene Ontology. Common conidial functions were found, but interestingly, pathogenicity factors and effectors were also identified. Upon analysis of the resulting proteins against the Pathogen-Host Interaction (PHI) database, 754 hits were identified. WideEffHunter and EffHunter effector predictors identified 618 effectors, 265 of them were shared with the PHI database. A total of 1107 conidial functions devoted to pathogenesis were found after our analysis. Regarding the conidial effectorome, it was found to comprise 40 canonical and 578 non-canonical effectors. Effectorome characterization revealed that RXLR, LysM, and Y/F/WxC are the largest effector families in the P. fijiensis conidial effectorome. Gene Ontology classification suggests that they are involved in many biological processes and metabolisms, expanding our current knowledge of fungal effectors.

Evaluation of different management practices against yellow Sigatoka disease of banana (Musa spp.) caused by Mycosphaerella musicola Leach

Leaf spot or yellow sigatoka disease (caused by Mycosphaerella musicola) of banana causes significant yield loss as well as in quality of fruits in every year and reported up to 65% or even more under favorable epidemic conditions. In this perspective, an experiment was conducted at ZBNF project Research field, college of horticulture, Sirsi (Uttara Kannada district of Karnataka) for consecutive two seasons (2020-21 & 2021-22). Experiment accompanied with RBD statistical design with five replications and four treatments. Four different management practices involving viz. Propiconazole 25EC @ 0.1% (Recommended package of practices-UHS, Bagalkot), Trichoderma harzianum 10g/lit. (Organic farming), sour butter milk 5 lit. per 200 liter of water (Natural farming) and Tebuconazole 50% + Trifloxystrobin 25% WG @0.5gm/lit. (Chemical farming) were evaluated against sigatoka leaf spot disease. Among the management practices, chemical farming comprises tebuconazole 50%+ trifloxystrobin 25%WG@1gm/lit was found effective in managing the disease (12.38% PDI) followed by recommended package of practice comprises propiconazole 25%EC @1ml/l (16.33% PDI), organic farming comprises of talk-based Trichoderma harzianum 10g/lit (17.33% PDI). Natural farming showed least effective to combat disease recorded maximum disease severity (19.66% PDI) after 210 days after planting. Although chemical farming can effectively control the disease but results in the serious risk on human health and environmental hazards. Therefore, organic and natural farming are an alternative approach that are eco-friendly and economically viable against sigatoka leaf spot disease management.

An efficient banana plant leaf disease classification using optimal ensemble deep transfer network

ABSTRACT Plants are a major source of food all around the world, and they are mainly affected by diseases caused by pathogens, insects, and parasitic plants. If the diseases are identified in the earlier stage, then it will be easy to apply pesticides and prevent the disease from further propagation. To recognise these diseases at earlier stages automatically, different researchers have established artificial intelligence-based approaches using machine learning and deep learning approaches which can identify the diseased lesions with high accuracy. In this paper, a novel Hybrid Moth Flame Optimization Algorithm-butterfly optimisation algorithm (HMFO-BOA) based optimal ensemble deep transfer network (OEDTN) classifier for banana leaf disease identification is developed. The OEDTN architecture offers increased predictability for banana leaf disease using Maximum Mean Discrepancy (MMD), ensemble learning, domain adaptation, and parameter transfer learning. The feature extraction is done by constructing different Deep Transfer Networks (DTN) via diverse kernel MMD. Finally, the DTNs are integrated via ensemble learning to obtain the final classification outcomes. The MFOBOA algorithm assigns optimal voting weights for each DTN to dynamically construct the OEDTN architecture. In the consequent testing process, the input banana leaf disease images are categorised into diverse classes such as BBW (Banana Bacterial Wilt Disease), BBS (Banana Black Sigatoka Disease), Cordana, pestalotiopsis, Sigatoka, and healthy. The experiments conducted on the Banana Leaf and BBW-BBS datasets prove that the proposed model offers improved performance than the state-of-art techniques.

Prevalence and Severity of Sigatoka Disease of Banana in Cameroon

Sigatoka Disease (SD) represents the major pre-harvest fungal disease of banana and also appear as the major constraint of banana production. This diseaseis caused by two major pathogenic fungi, Pseudocercospora fijiensis and P. musicola. It provokes necrotic lesions on leaves and serious infestations leads to a substantial reduction in the green leaf area of infected plants and thus the photosynthetic area of the plant and consequently leads to yield loss. In the present work, three plantations were surveyed and experiments conducted in Southwest Cameroon to assess the prevalence and severity of sigatoaka disease in the banana plantations. Our results revealed a 100 % prevalence of Sigatoka in the field with an incidence of 59 %. In a severe manner, the severity in all the plantations was seen to have a constant increase with the highest severity at 23.83 % observed in plantation 1. In a nutshell, the incidence and severity of the disease was seen to increase with respect to time and season.

The genome of Bacillus tequilensis EA-CB0015 sheds light into its epiphytic lifestyle and potential as a biocontrol agent.

Different Bacillus species have successfully been used as biopesticides against a broad range of plant pathogens. Among these, Bacillus tequilensis EA-CB0015 has shown to efficiently control Black sigatoka disease in banana plants, presumably by mechanisms of adaptation that involve modifying the phyllosphere environment. Here, we report the complete genome of strain EA-CB0015, its precise taxonomic identity, and determined key genetic features that may contribute to its effective biocontrol of plant pathogens. We found that B. tequilensis EA-CB0015 harbors a singular 4 Mb circular chromosome, with 3,951 protein-coding sequences. Multi-locus sequence analysis (MLSA) and average nucleotide identity (ANI) analysis classified strain EA-CB0015 as B. tequilensis. Encoded within its genome are biosynthetic gene clusters (BGCs) for surfactin, iturin, plipastatin, bacillibactin, bacilysin, subtilosin A, sporulation killing factor, and other natural products that may facilitate inter-microbial warfare. Genes for indole-acetic acid (IAA) synthesis, the use of diverse carbon sources, and a multicellular lifestyle involving motility, biofilm formation, quorum sensing, competence, and sporulation suggest EA-CB0015 is adept at colonizing plant surfaces. Defensive mechanisms to survive invading viral infections and preserve genome integrity include putative type I and type II restriction modification (RM) and toxin/antitoxin (TA) systems. The presence of bacteriophage sequences, genomic islands, transposable elements, virulence factors, and antibiotic resistance genes indicate prior occurrences of genetic exchange. Altogether, the genome of EA-CB0015 supports its function as a biocontrol agent against phytopathogens and suggest it has adapted to thrive within phyllosphere environments.

BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases

All over the world, bananas are one of the most common fruits. It accounts for nearly 16% of global fruit production. However, every year, a large amount of banana yield losses occur due to different diseases of the banana leaf. It is essential to identify these diseases at an early stage in order to increase banana production. A visual inspection is the most common method of identifying banana leaf diseases. With a visual inspection, errors are common, time is a factor, and expertise is required. This study shows how deep learning and Bayesian optimization can be used to effectively diagnose banana leaf diseases from images without any human intervention. We collected the Banana Leaf Spot Diseases (BananaLSD) dataset from various locations in Bangladesh. The dataset consists of images of three banana leaf diseases: Pestalotiopsis, Sigatoka, and Cordana. Our proposed BananaSqueezeNet model performed exceptionally well in diagnosing banana leaf diseases from the images with an overall accuracy of 96.25%, precision of 96.53%, recall of 96.25%, specificity of 98.75%, F1-score of 96.17%, and MCC of 95.13%. The BananaSqueezeNet model outperforms some state-of-the-art convolutional neural networks that include EfficientNetB0, MobileNetV3, ResNet-101, ResNet-50, InceptionNet-V3, and VGG16. The BananaSqueezeNet model also detected seven other diseases that affect banana leaves, fruits, and stems, including banana fruit scarring beetle, black sigatoka, bacterial soft rot, pseudo stem weevil, yellow sigatoka, banana aphids, and panama disease, with an accuracy of 95.13%. BananaSqueezeNet will enable banana growers to detect banana diseases early, and we hope that it will ultimately lead to an increase in banana production in Bangladesh and around the world.

Efficacy of Botanical Mixture and Fungicides to Combat Sigatoka Disease in Banana Cultivation

Black Sigatoka disease caused by Mycosphaerella fijiensis seriously threatens banana cultivation worldwide. This infirmity is controlled by the recurrent application of synthetic fungicides, which leads to several adverse effects. Plant extracts with antifungal potential have long been investigated as they contain secondary bioactive compounds for protecting crops from microbial infection. This study compared the antifungal activities of both botanical mixtures and recommended fungicides to control Black Sigatoka in banana. Laboratory and field studies were conducted to assess the antifungal activity of the botanical mix prepared from eight different herbal plant parts against M. fijiensis, with two recommended fungicides as the positive control. In vitro results showed that the radial growth of the fungus was significantly impaired (P<0.05.) by the addition of the botanical mixture, fungicides, and combination of the botanical mixture (BM) and fungicides in the PDA media. Mycosphaerella fijiensis differed in their reaction to the BM and fungicides, and combination of BM and fungicides, but on the whole, growth inhibition increased with the concentration. In field experiments, all the treatments reduced the disease incidence significantly compared to the unsprayed control, where the disease severity index (DSI) increased from 43.88 to 67.77 within 28 days. After the first spray, the botanical mixture and fungicides (Chlorothalonil and Carbendazim) reduced the disease severity index to 39.7, 41.19, and 44.44, respectively. Fungicides and combinations of Chlorothalonil + BM and Carbendazim + BM showed the lowest DSI, 34.07 and 35.83, respectively. The results show that combining the synthetic and botanical mixture is more effective for controlling the disease. The study reveals the possibility of utilizing a botanical mixture as an integrated approach to manage Black Sigatoka disease more efficiently and in an eco-friendly manner.