New treasures in Cordycipitaceae: Fungicolous fungi associated with Pseudocercospora fijiensis and P. musae in Brazil, including Matutinistella gen. nov.

Black Sigatoka, also known as black leaf streak, is caused by Mycosphaerella fijiensis Morelet (anamorph Pseudocercospora fijiensis (Morelet) Deighton). It is the most significant disease of bananas and plantains (Musa spp.) because most of the economically important cultivars of exported and staple commodities are highly susceptible. The Caribbean is one of the few regions of the world where black Sigatoka is not widespread. Black Sigatoka has been reported in the Bahamas, Cuba, Hispaniola, and Jamaica (2). Yellow Sigatoka, caused by M. musicola Leach (anamorph P. musae (Zimm.) Deighton), has been recognized in Puerto Rico since 1938-1939 (3). In August 2004, symptoms resembling black Sigatoka were first observed in Añasco, Puerto Rico by extension personnel from the University of Puerto Rico. Since black and yellow Sigatoka produce similar disease symptoms, a survey was conducted in the western banana- and plantain-production region of Puerto Rico to confirm the presence of black Sigatoka. Leaf samples were collected from production fields near the towns of Las Marias, Maricao, and Añasco. Single-ascospore isolates were recovered using the discharge technique from moistened pseudothecia in necrotic lesions that were inverted over water agar, and ascospores were transferred to potato dextrose agar. The isolates were subcultured in potato dextrose broth for mycelium production. DNA was isolated from mycelium with the FastDNA kit (Q-Biogen, Irvine, CA) for 19 isolates. Internal transcribed spacer as well as the 5.8s rDNA regions were polymerase chain reaction amplified with primers specific to M. fijiensis or M. musicola (1). Amplification products (˜1,100 bp) were observed for 18 of the 19 isolates, 6 of which were M. fijiensis and the remaining 12 were M. musicola, while the positive controls for both species were also amplified with the respective primer pairs. M. fijiensis was recovered from production fields close to all three towns. The source of M. fijiensis in Puerto Rico is unclear, but it may have originated from introduced leaf material and/or wind dispersed ascospores from neighboring countries. The presence of black Sigatoka in Puerto Rico will most likely increase production costs where fungicide applications will be needed to maintain yields. The USDA-ARS, Tropical Agriculture Research Station is the official Musa spp. germplasm repository for the National Plant Germplasm System. As such, efforts are underway to introduce and evaluate black Sigatoka disease-resistant clones that can satisfy local and export market criteria.

The Sigatoka leaf spot complex on Musa spp. includes three major pathogens: Pseudocercospora, namely P. musae (Sigatoka leaf spot or yellow Sigatoka), P. eumusae (eumusae leaf spot disease), and P. fijiensis (black leaf streak disease or black Sigatoka). However, more than 30 species of Mycosphaerellaceae have been associated with Sigatoka leaf spots of banana, and previous reports of P. musae and P. eumusae need to be re-evaluated in light of recently described species. The aim of the present study was thus to investigate a global set of 228 isolates of P. musae, P. eumusae and close relatives on banana using multigene DNA sequence data [internal transcribed spacer regions with intervening 5.8S nrRNA gene (ITS), RNA polymerase II second largest subunit gene (rpb2), translation elongation factor 1-alpha gene (tef1), beta-tubulin gene (tub2), and the actin gene (act)] to confirm if these isolates represent P. musae, or a closely allied species. Based on these data one new species is described, namely P. pseudomusae, which is associated with leaf spot symptoms resembling those of P. musae on Musa in Indonesia. Furthermore, P. eumusae, P. musae and P. fijiensis are shown to be well defined taxa, with some isolates also representing P. longispora. Other genera encountered in the dataset are species of Zasmidium (Taiwan leaf speckle), Metulocladosporiella (Cladosporium leaf speckle) and Scolecobasidium leaf speckle. Citation: Crous P, Carlier J, Roussel V, Groenewald JZ (2020). Pseudocercospora and allied genera associated with leaf spots of banana (Musa spp.). Fungal Systematics and Evolution 7: 1-19. doi: 10.3114/fuse.2021.07.01.

Banana (Musa spp.) crops face severe yield and economic losses due to foliar diseases such as Moko disease and Black Sigatoka. In Ecuador, Moko outbreaks have increasingly devastated banana plantations, threatening one of the country’s most important export commodities and putting significant pressure on local producers and the national economy. Traditional field inspection methods are labor-intensive, subjective, and often ineffective for timely disease detection and containment. In this study, we propose an improved deep learning-based segmentation approach using YOLOv8 architectures to automatically detect and segment Moko and Black Sigatoka infections from unmanned aerial vehicle (UAV) imagery. Multiple YOLOv8 configurations were systematically analyzed and compared, including variations in backbone depth, model size, and hyperparameter tuning, to identify the most robust setup for field conditions. The final optimized configuration achieved a mean precision of 79.6%, recall of 80.3%, mAP@0.5 of 84.9%, and mAP@0.5:0.95 of 62.9%. The experimental results demonstrate that the improved YOLOv8 segmentation model significantly outperforms previous classification-based methods, offering precise instance-level localization of disease symptoms. This study provides a solid foundation for developing UAV-based automated monitoring pipelines, contributing to more efficient, objective, and scalable disease management strategies.

Most banana cultivars are susceptible to many diseases, whereas Sigatoka leads to greatest yield losses. One of the strategies to overcome this disease is thorough banana genetic breeding which consists in the obtainment of improved (AA) diploids which are then crossed with triploids obtaining (AAAB) tetraploid disease resistant bananas also presenting other important agronomic characteristics. The prior knowledge of the genetic diversity of (AA) diploids, is therefore considered indispensable in order to direct the crosses being made. The objective of the present work was to analyze the genetic diversity of 20 (AA) banana diploids with contrasting levels of reaction to yellow and black Sigatoka caused by Mycosphaerella musicola and M. fijensis, respectively, using molecular markers. From the dendrogram data it is shown that a great number of experimental hybrids can be obtained from the combination of genetically different diploids, therefore making the banana genetic breeding program more efficient regarding its objectives.

Black Sigatoka, caused by the fungus P. fijiensis, is the most severe disease that affects bananas (Musa spp). Research has projected increases in disease severity in response to climate change and variability, highlighting the need to analyze the relative contributions of climate change and immediate responses to their effects on these crops. This study aimed to analyze the influence of climate variability and spatiotemporal variability of soil and climatic conditions on Black Sigatoka. In addition, it was evaluated the use of geostatistical, geomatics, remote sensing, and geographic information systems techniques for disease detection over the past 30 years. A systematic review of 156 articles was conducted using bibliometric analysis, considering descriptive statistics and bibliometric mapping using VOSviewer. The results showcased geostatistical methods used to measure Sigatoka infection in banana crops and identify soil and climatic variables associated with this disease. It is concluded that climate change has the potential to increase Black Sigatoka infection, but precision agriculture could be an effective tool to mitigate the negative impact on banana crops.

Banana production is threatened by Black Sigatoka disease caused by the fungus Pseudocercospora fijiensis (M. Morelet) Deighton, which is considered one of the most destructive diseases of this crop. Black Sigatoka control primarily relies on the use of chemical fungicides, which increases production costs and may have negative impacts on health and the environment when applied inappropriately. In addition, their extensive use may select for resistant strains, causing reduced efficacy of fungicides. Therefore, alternative control options for Black Sigatoka are urgently needed. In the present study, we evaluated plant resistance inducers as an alternative for disease management. The effect of resistance inducers on the growth and development of P. fijiensis and on banana defence-related gene expression was measured. Banana plots were treated with five resistance inducers and the best three were included in a commercial programme of Black Sigatoka management. Each resistance inducer, either applied individually or mixed with standard fungicides, showed significant reductions of Black Sigatoka severity when compared to the control. It was confirmed that resistance inducers reduced in vitro growth and development of P. fijiensis as shown for other pathogens. Banana defence-related genes were found to be up-regulated after application of resistance inducers. These results suggest that the reduction of disease severity in banana crops after application of resistance inducers may be due to a direct effect on P. fijiensis in addition to the activation of plant responses. Resistance inducers are a potentially effective alternative to Black Sigatoka disease management.

Two sigatoka leaf‐spot diseases affect banana and plantain: yellow sigatoka and black sigatoka. These are caused by two closely related and morphologically similar species of fungi: yellow sigatoka by Mycosphaerella musicola, and black sigatoka by M. fijiensis. Correct identification of these pathogens is hampered by the ambiguity of symptoms on different cultivars, and the difficulty in successfully isolating the pathogens in pure culture. Subsequent identification relies on sporulation which may also be difficult to induce. A diagnostic test based on the polymerase chain reaction (PCR) was therefore developed for these pathogens. The internally transcribed spacer (ITS1) region of ribosomal DNA (rDNA) of several isolates of M. fijiensis, M. musicola, and M. musae was sequenced, and a 21‐base oligonucleotide primer constructed for each species from a variable region identified in the sequences. These primers were used with primers in the conserved regions of the rDNA to produce a species‐specific product for each of the three species using PCR. The primers also amplified a fungal product from single ascospores placed in the PCR reaction, and from DNA from banana leaf tissue showing disease symptoms. No PCR products were produced when DNA from other species commonly found on banana was amplified. The fungi have been detected in herbarium samples up to 3 years old. The technique is very sensitive, and is able to detect DNA in a 1:20000 dilution of DNA extracted from 50 mg of freeze‐dried fungal mycelium.

There are three major leaf diseases, now called Pseudocercospora leaf spot diseases of banana, which form the sigatoka leaf spot complex: Sigatoka disease (yellow Sigatoka) caused by Pseudocercospora musae, black leaf streak disease (black Sigatoka) caused by P. fijiensis and Eumusae leaf spot disease caused by P. eumusae. These diseases, especially black leaf streak disease, are a major constraint to banana cultivation and they occur across most of the global production zones of banana. This chapter gives an overview of the symptoms of the diseases, their taxonomy and diagnosis, population history and geographical distribution and provides some new insights into the disease cycle, epidemiology and the host–pathogen interactions which are required to underpin the development of more sustainable methods to control the disease through field management and the identification and deployment of effective resistance in the banana plant.

Black Leaf Streak Disease (BLSD) is the most restricting leaf disease to banana tree cultivation around the world. In order to control this disease, synthetic fungicides are extensively used. However, these products pose a real danger to environmental pollution and the health of applicators and consumers. Faced with this situation, alternative solutions must be considered to overcome their systematic use. This study was initiated in this context so as to assess the effectiveness of 20 biopesticide formulations on <i>Mycosphaerella fijiensis</i> conidia stemming from banana tree leaf samples originating from village plantations and showing the typical symptoms of stage 2 or 3 black Sigatoka. The assessment method used was that of dispersion in solid medium. Observations were made under an optical microscope equipped with a camera and consisted in determining the inhibition rates of conidia germ tube growth. A pathogenicity test was performed with 8 <i>Mycosphaerella</i> spp. isolates according to an inoculation technique under controlled conditions on whole plants of 5 banana tree cultivar vivoplants. The assessment of biopesticide protection effectiveness against BLSD was conducted on cultivar "Orishele" (very susceptible) with the most aggressive and virulent strain selected during isolate pathogenicity test. The results obtained show that all biopesticide formulations have significant antifungal activity on <i>M. fijiensis</i> conidia germ tube elongation. The average inhibition rate ranged from 83.31 to 99.89% for all biopesticides. The 8 <i>M. fijiensis</i> isolates used have all raised symptoms characteristic of black leaf streak disease regardless of the cultivar. In contrast, no isolate caused symptoms characteristic of Sigatoka disease (Yellow Sigatoka). Biopesticides and synthetic fungicides significantly reduced disease development rate compared to inoculated and untreated controls, but at varying degrees. Preventive treatment of seedlings is found to be much more effective than curative treatment. However, in order to protect banana and plantain tree varieties against <i>M. fijiensis</i>, both types of treatments are necessary.

Use of PCR for detection of Mycosphaerella fijiensis and M. musicola, the causal agents of Sigatoka leaf spots in banana and plantain

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.

The sterol demethylation inhibitors (DMIs) are among the most widely used fungicides for controlling black Sigatoka (Mycosphaerella fijiensis) and yellow Sigatoka (Mycosphaerella musicola) in banana plantations in Brazil. Black Sigatoka is considered more important due to causing yield losses of up to 100% in commercial banana crops under predisposing conditions. In contrast, yellow Sigatoka is important due to its widespread occurrence in the country. This study aimed to determine the current sensitivity levels of Mf and Mm populations to DMI fungicides belonging to the chemical group of triazoles. Populations of both species were sampled from commercial banana plantations in Registro, Vale do Ribeira, São Paulo (SP), Ilha Solteira, Northwestern SP, and Janaúba, Northern Minas Gerais, and were further characterized phenotypically. Additionally, allelic variation in the CYP51 gene was analyzed in populations of these pathogens to identify and characterize major mutations and/or mechanisms potentially associated with resistance. Sensitivity to the triazoles propiconazole and tebuconazole was determined by calculating the 50% inhibitory concentration of mycelial growth (EC50) based on dose–response curves ranging from 0 to 5 µg mL−1. Variation in sensitivity to fungicides was evident with all nine Mf isolates showing moderate resistance levels to both propiconazole or tebuconazole, while 11 out of 42 Mm strains tested showed low to moderate levels of resistance to these triazoles. Mutations leading to CYP51 substitutions Y136F, Y461N/H, and Y463D in Mm and Y461D, G462D, and Y463D in Mf were associated with low or moderate levels of resistance to the triazoles. Interestingly, Y461H have not been reported before in Mm or Mf populations, and this alteration was found in combination with V106D and A446S. More complex CYP51 variants and CYP51 promoter inserts associated with upregulation of the target protein were not detected and can explain the absence of highly DMI-resistant strains in Brazil. Disease management programs that minimize reliance on fungicide sprays containing triazoles will be needed to slow down the further evolution and spread of novel CYP51 variants in Mf and Mm populations in Brazil.

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.

A DOSE-RESPONSE APPROACH DIFFERENTIATING VIRULENCE OF MYCOSPHAERELLA FIJIENSIS STRAINS ON BANANA LEAVES USES EITHER SPORES OR MYCELIA AS INOCULA

Aims: The present study was carried out to determine the diversity of endophytic fungi that colonize the leaves of Psidium guajava, and to evaluate their antagonistic activity against Fusarium oxysporum f.sp. cubense and Mycosphaerella fijiensis which are the two main phytopathogens of banana plants.&#x0D; Place and Duration of Study: The research was carried out at Microbiology Laboratory, Faculty of Sciences, University of Yaoundé I and Microbiology Laboratory, Faculty of Biotechnologies, University of Agronomic Sciences and Veterinary Medicine Bucharest, between April 2018 and February 2020.&#x0D; Methodology: Fragments of surface sterilized leaves of Psidium guajava were inoculated on Potato Dextrose Agar supplemented with chloramphenicol. The isolated and purified endophytic fungi were identified based on their macroscopic and microscopic characters using a mycological atlas as guide. The non-sporulating isolates were identified by comparing the ITS regions of their DNA to those of known fungi registered in the GenBank database. The antagonistic activity of the endophytic fungi isolated against Fusarium oxysporum and Mycosphaerella fijiensis was screened using dual culture method.&#x0D; Results: A total of 28 endophytic fungal were isolated from the leaves of Psidium guajava corresponding to a colonization frequency of 33.33%. These isolates were identified as: Aspergillus sp., Botryosphaeria sp., Fusarium sp., Neoscytalidium sp., Xylaria sp., Phyllosticta capitalensis, Cercospora apii, Xylaria longipes, Phomopsis sp., Phomopsis asparagi, Aspergillus versicolor, Pallidocercospora thailandica, and Xylaria grammica that belonged to the Deuteromycota and Ascomycota divisions. These endophytic fungi inhibited the growth of Fusarium oxysporum f.sp. cubense and Mycosphaerella fijiensis with the percentage inhibition varying respectively from 23.25% to 73.52% and from 21.36% to 100%. The species Botryosphaeria sp., Phomopsis sp., Phomopsis asparagi, and Xylaria longipes exhibited the greatest activity.&#x0D; Conclusion: The leaves of Psidium guajava have a fairly varied diversity of endophytic fungi. These endophytic fungi can serve as potential biological control agents against Panama and Sigatoka diseases of banana and also would produce secondary metabolites with antifungal properties.