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Journal articles on the topic 'Black Sigatoka disease'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kambo, Edwin, Devotha Nyambo, Judith Leo, and Mussa Ally. "A Deep Learning Model for Classifying Black Sigatoka Disease in Banana Leaves Based on Infection Stages." Indian Journal Of Science And Technology 17, no. 37 (2024): 3889–97. http://dx.doi.org/10.17485/ijst/v17i37.2453.

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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 opt

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Reuveni, Moshe, Marcel Barbier, and Agnelo J. Viti. "Essential Tea Tree Oil As a Tool to Combat Black Sigatoka in Banana." Outlooks on Pest Management 31, no. 4 (2020): 180–86. http://dx.doi.org/10.1564/v31_aug_08.

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Black Sigatoka disease, caused by Mycosphaerella fijiensis, is considered the most damaging and costly disease of commercial banana and plantain and is mainly controlled by intensive sprays of synthetic fungicides. Essential tea tree oil derived from Melaleuca alternifolia plant was found to be effective against a wide range of plant pathogenic fungi including black Sigatoka in conventional production systems and was as effective as synthetic fungicides such as tridemorph, difenoconazole, trifloxystrobin and azoxystrobin. This paper provides evidence that tea tree oil offers an attractive alte

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Edwin, Kambo, Nyambo Devotha, Leo Judith, and Ally Mussa. "A Deep Learning Model for Classifying Black Sigatoka Disease in Banana Leaves Based on Infection Stages." Indian Journal of Science and Technology 17, no. 37 (2024): 3889–97. https://doi.org/10.17485/IJST/v17i37.2453.

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Abstract <strong>Objective:</strong> This research study aims to develop an efficient deep-learning model to detect and classify stages of Black Sigatoka disease in banana plants. <strong>Methods:</strong> 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, re

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Irish, B. M., R. Goenaga, and R. C. Ploetz. "Mycosphaerella fijiensis, Causal Agent of Black Sigatoka of Musa spp. Found in Puerto Rico and Identified by Polymerase Chain Reaction." Plant Disease 90, no. 5 (2006): 684. http://dx.doi.org/10.1094/pd-90-0684a.

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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

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Torres Ustate, Luis Miguel, Nelson Virgilio Piraneque Gambasica, and Martha Ligia Castellanos Martínez. "Role of Precision Agriculture in Mitigating Black Sigatoka in Banana Cultivation Under Climate Change: A Review and Bibliometric Analysis." TecnoLógicas 27, no. 61 (2024): e3158. https://doi.org/10.22430/22565337.3158.

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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 informat

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Cavero, Poholl Adan Sagratzki, Rogério Eiji Hanada, Luadir Gasparotto, Rosalee Albuquerque Coelho Neto, and Jorge Teodoro de Souza. "Biological control of banana black Sigatoka disease with Trichoderma." Ciência Rural 45, no. 6 (2015): 951–57. http://dx.doi.org/10.1590/0103-8478cr20140436.

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Black Sigatoka disease caused by Mycosphaerella fijiensis is the most severe banana disease worldwide. The pathogen is in an invasive phase in Brazil and is already present in most States of the country. The potential of 29 isolates of Trichoderma spp. was studied for the control of black Sigatoka disease under field conditions. Four isolates were able to significantly reduce disease severity and were further tested in a second field experiment. Isolate 2.047 showed the best results in both field experiments and was selected for fungicide sensitivity tests and mass production. This isolate was

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Vanlalruata, l. H. Jonathan, and Hijam Meronbala Devi. "Survey and Identification of Fungal Plant Diseases on Major Crops in Lunglei District of Mizoram." Environment and Ecology 42, no. 4A (2024): 1688–98. http://dx.doi.org/10.60151/envec/khro7188.

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A survey work on fungal diseases of major crops in Lunglei district, Mizoram was undertaken in farmers’ field during kharif season of 2021, using visual assessment method, in which pictorial representation of the host plant with known and graded amount of disease are compared with disease leaves to allow estimation of disease incidence, by measuring intensity of the pathogen with the help of disease grading scales. The disease samples collected from the farmers’ field were brought in the laboratory where identification of pathogen was done based on their morphological characters under the micr

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8

Alamo, Carmen, Edward Evans, Alba Brugueras, and Sikavas Nalampang. "Economic Impact and Trade Implications of the Introduction of Black Sigatoka (Mycosphaerella fijiensis) into Puerto Rico." Journal of Agricultural and Applied Economics 39, s1 (2007): 5–17. http://dx.doi.org/10.1017/s107407080002890x.

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This article addresses the issues of the potential impacts of the introduction of black sigatoka into Puerto Rico under situations in which the government assists growers in managing the spread of the disease, with and without prohibitions on imports of plantains and bananas. An equilibrium displacement model is used to quantify the impact of black sigatoka. The results indicate that under both scenarios the net economic benefits to society were negative. Over the long term, the government would be well-advised to invest in research to develop plantain and banana varieties that are resistant t

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Escudero, Cristian A., Andrés F. Calvo, and Arley Bejarano. "Black Sigatoka Classification Using Convolutional Neural Networks." International Journal of Machine Learning and Computing 11, no. 4 (2021): 323–26. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1055.

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In this paper we present a methodology for the automatic recognition of black Sigatoka in commercial banana crops. This method uses a LeNet convolutional neural network to detect the progress of infection by the disease in different regions of a leaf image; using this information, we trained a decision tree in order to classify the level of infection severity. The methodology was validated with an annotated database, which was built in the process of this work and which can be compared with other state-of-the-art alternatives. The results show that the method is robust against atypical values

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Kumakech, Alfred, Laban F. Turyagyenda, Richard Edema, and Patrick Okori. "Pseudocercospora fijiensis mycelia-based infection system enhances investigational efficacy of P. fijiensis-banana pathosystem." Brazilian Journal of Science 3, no. 2 (2024): 143–49. http://dx.doi.org/10.14295/bjs.v3i2.485.

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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 geno

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Kimunye, Janet, Kennedy Jomanga, Anthony Fredrick Tazuba, et al. "Genotype X Environment Response of ‘Matooke’ Hybrids (Naritas) to Pseudocercospora fijiensis, the Cause of Black Sigatoka in Banana." Agronomy 11, no. 6 (2021): 1145. http://dx.doi.org/10.3390/agronomy11061145.

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Growing bananas resistant to Pseudocercospora fijiensis, the cause of black Sigatoka, is the preferred disease control strategy for resource-poor farmers. Banana breeding programs in east Africa have developed 27 Matooke hybrids (commonly known as NARITAs) with higher yields than local landraces. To assess the response of NARITA hybrids to P. fijiensis, 22 hybrids were evaluated under natural field conditions in four locations—Kawanda and Mbarara in Uganda, and Maruku, and Mitarula in Tanzania—between 2016 and 2018 for three crop cycles. Black Sigatoka was visually assessed and the area under

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Segall, Richard S., Soichiro Takahashi, and Prasanna Rajbhandari. "Big Data Visualization for Black Sigatoka Disease of Bananas and Pathogen–Host Interactions (PHI) of Other Plants." International Journal of Applied Research in Bioinformatics 13, no. 1 (2024): 1–22. https://doi.org/10.4018/ijarb.361940.

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Black sigatoka is a leaf spot disease affecting banana plants that has caused significant yield reductions of up to 50% (Arman et al., 2023). This research presents data visualizations of 8,761 data points related to black sigatoka in banana plants, encompassing attributes such as time, canopy temperature, and relative humidity. The paper also reviews related work, including the application of data mining to plant studies, the use of deep learning neural networks for plant disease data, and the use of machine learning for predicting crop yields and detecting disease. Additionally, it presents

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Brice Sidoine Essis, Demby Laetitia Muriel Kouadio, Klotioloma Coulibaly, Siaka Traoré, and Konan Evrard Brice Dibi. "Evaluation of the effectiveness of the main fungicides used against Mycosphaerella fijiensis of banana plantations in Côte d’Ivoire." World Journal of Advanced Research and Reviews 18, no. 1 (2023): 1080–86. http://dx.doi.org/10.30574/wjarr.2023.18.1.0726.

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Black Sigatoka, caused by Mycosphaerella fijiensis Morelet (Pseudocercospora fijiensis [Morelet] Deighton), is one of the most devastating diseases of banana. In commercial banana cropping systems, Black Sigatoka is mainly managed by fungicides. The emergence of resistant strains of M. fijiensis to commonly used fungicides has necessitated an evaluation of their efficacy on untreated (wild) populations. Our study on the effectiveness of the main fungicide molecules used in Côte d’Ivoire in the control of black leaf streak disease (BLSD) was conducted to develop new sustainable control strategi

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14

Bonfim Junior, Mauro Ferreira, Álvaro José Back, Márcio Sônego, Joelma Miszinski, and André Boldrin Beltrame. "Proposal of a weather-based prediction system for Yellow Sigatoka in Santa Catarina State, Brazil." Agropecuária Catarinense 37, no. 3 (2024): 63–68. https://doi.org/10.52945/rac.v37i3.1862.

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Banana (Musa spp.) is one of the main fruits produced in Brazil, and its production is among the largest in the world. The Sigatoka disease complex is widely distributed across banana producing regions mainly comprising the fungi Pseudocercospora fijiensis and P. musae, causal agents of Black and Yellow Sigatoka, respectively. This study observed the epidemiological factors affecting the development of Yellow Sigatoka disease in a banana plantation not sprayed with fungicides in the subtropical climate of Santa Catarina, Brazil, to propose a prediction system for the disease. Yellow Sigatoka s

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Bebber, Daniel P. "Climate change effects on Black Sigatoka disease of banana." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1775 (2019): 20180269. http://dx.doi.org/10.1098/rstb.2018.0269.

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Climate change has significantly altered species distributions in the wild and has the potential to affect the interactions between pests and diseases and their human, animal and plant hosts. While several studies have projected changes in disease distributions in the future, responses to historical climate change are poorly understood. Such analyses are required to dissect the relative contributions of climate change, host availability and dispersal to the emergence of pests and diseases. Here, we model the influence of climate change on the most damaging disease of a major tropical food plan

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Kablan, L., A. Lagauche, B. Delvaux, and A. Legr`ve. "Silicon Reduces Black Sigatoka Development in Banana." Plant Disease 96, no. 2 (2012): 273–78. http://dx.doi.org/10.1094/pdis-04-11-0274.

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The effect of silicon (Si) uptake on the susceptibility of Musa acuminata to Mycosphaerella fijiensis was investigated in three experiments conducted under controlled conditions. Plants were grown in the presence of Si or not, in pots adapted for a hydroponic culture system or in pots filled with compost. The banana leaves were inoculated after 4 or 6 months of plant growth by spraying conidial suspensions or by brushing mycelia fragments. The disease progress over time was assessed using quantitative and qualitative scales. At the end of each experiment, disease severity was also analyzed usi

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Tejerina, J. C., G. Meriles, R. H. Stover, R. C. Ploetz, and S. Romanoff. "First Report of Black Sigatoka in Bolivia." Plant Disease 81, no. 11 (1997): 1332. http://dx.doi.org/10.1094/pdis.1997.81.11.1332c.

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Black Sigatoka, caused by Mycosphaerella fijiensis, is the most important disease of banana worldwide (1). It affects cultivars of the Cavendish subgroup that are used for export and important, locally consumed cooking and dessert bananas and plantains, reducing yields by 50% or more. Black Sigatoka first appeared in the Western Hemisphere in 1972 in Honduras, and has spread to all other countries in Central America (1980), Mexico (1980), and the following islands in the Caribbean: Cuba (1992), Hispanola (Dominican Republic) (1996), and Jamaica (1994). In South America, the disease has spread

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18

Vázquez-Euán, Chi-Manzanero, Hernández-Velázquez, et al. "Identification of New Hosts of Pseudocercospora fijiensis Suggests Innovative Pest Management Programs for Black Sigatoka Disease in Banana Plantations." Agronomy 9, no. 10 (2019): 666. http://dx.doi.org/10.3390/agronomy9100666.

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Black Sigatoka is the main constraint to banana production worldwide, and epidemic outbreaks are continuously causing huge losses. Successful management of diseases requires a profound knowledge of the epidemiological factors that influence disease dynamics. Information regarding alternative hosts of Pseudocercospora fijiensis, the causal agent, is still very scarce. To date, only Heliconia psittacorum has been reported as an alternative plant host, and we hypothesized that other plants can house P. fijiensis. In the present report, ten plant species with suspicious leaf spots were collected i

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Ugarte Fajardo, Jorge, María Maridueña-Zavala, Juan Cevallos-Cevallos, and Daniel Ochoa Donoso. "Effective Methods Based on Distinct Learning Principles for the Analysis of Hyperspectral Images to Detect Black Sigatoka Disease." Plants 11, no. 19 (2022): 2581. http://dx.doi.org/10.3390/plants11192581.

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Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set

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Brice, Sidoine Essis, Laetitia Muriel Kouadio Demby, Coulibaly Klotioloma, Coulibaly Klotioloma, and Evrard Brice Dibi Konan. "Evaluation of the effectiveness of the main fungicides used against Mycosphaerella fijiensis of banana plantations in Côte d'Ivoire." World Journal of Advanced Research and Reviews 18, no. 1 (2023): 1080–86. https://doi.org/10.5281/zenodo.8179096.

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Black Sigatoka, caused by <em>Mycosphaerella fijiensis</em> Morelet (<em>Pseudocercospora fijiensis</em> [Morelet] Deighton), is one of the most devastating diseases of banana. In commercial banana cropping systems, Black Sigatoka is mainly managed by fungicides. The emergence of resistant strains of <em>M. fijiensis</em> to commonly used fungicides has necessitated an evaluation of their efficacy on untreated (wild) populations. Our study on the effectiveness of the main fungicide molecules used in Côte d’Ivoire in the control of black leaf streak disease

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Tovar-Martínez, Yurley Tatiana, Arley Bejarano-Martínez, and Andrés Felipe Calvo-Salcedo. "Mobile application for the detection of black Sigatoka." Visión electrónica 14, no. 1 (2020): 111–18. http://dx.doi.org/10.14483/22484728.15906.

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Black Sigatoka is one of the main problems that affect the quality and production of the banana crop, it´s because of this, the development of systems to detect diseases, generate an important tool for the monitoring and control carried out by the farmer. The proposed system leverages hardware on mobile devices to implement computer vision techniques to determine the percentage of affected area of the plant. The smartphone is used to acquire data and capture the disease through images. The detection of diseased pixels is then performed through a segmentation algorithm with histogram anal

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Ploetz, R. C., and X. Mourichon. "First Report of Black Sigatoka in Florida." Plant Disease 83, no. 3 (1999): 300. http://dx.doi.org/10.1094/pdis.1999.83.3.300c.

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Black Sigatoka, caused by Mycosphaerella fijiensis, is widely recognized as the most important disease of banana, Musa spp. It has spread rapidly in the Western Hemisphere since it first appeared in Honduras in 1972, and is now found in the Caribbean basin in Cuba, Jamaica, and the Dominican Republic, and on the mainland from central Mexico south to Bolivia and northwestern Brazil (2). In October 1998, symptoms of black Sigatoka (2) were observed on several different cultivars in a collection at the University of Florida's Tropical Research and Education Center (TREC) in Homestead (25°30′ N, 8

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Amarillas, Luis, Mitzi Estrada-Acosta, Rubén G. León-Chan, Carlos López-Orona, and Luis Lightbourn. "First Draft Genome Sequence Resource of a Strain of Pseudocercospora fijiensis Isolated in North America." Phytopathology® 110, no. 10 (2020): 1620–22. http://dx.doi.org/10.1094/phyto-04-20-0121-a.

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Black Sigatoka disease, caused by the fungus Pseudocercospora fijiensis, is one of the most devastating diseases of banana around the world. Fungicide applications are the primary tool used to manage black Sigatoka, but fungicide resistance in P. fijiensis, as in other fungal pathogens, is one of the major limitations in the efficient management and prevention of this disease. In the current study, we present the draft genome of P. fijiensis strain IIL-20, the first genomic sequence published from a strain of this fungus isolated in North America. Bioinformatic analysis showed putative genes i

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Vera-Velez, Roy, Raul Ramos-Veintimilla, and Jorge Grijalva-Olmedo. "Optimizing Pathogen Control through Mixed Cocoa–Plantain Agroecosystems in the Ecuadorian Coastal Region." Agronomy 14, no. 6 (2024): 1107. http://dx.doi.org/10.3390/agronomy14061107.

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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

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Godoy, Rossana Catie Bueno de, Nina Waszczynskj, Fernanda Alves Santana, Sebastião de Oliveira e. Silva, Luciana Alves de Oliveira, and Guilherme Godoy dos Santos. "Physico-chemical characterization of banana varieties resistant to black leaf streak disease for industrial purposes." Ciência Rural 46, no. 9 (2016): 1514–20. http://dx.doi.org/10.1590/0103-8478cr20150905.

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ABSTRACT: Cultivated bananas have very low genetic diversity making them vulnerable to diseases such as black-Sigatoka leaf spot. However, the decision to adopt a new banana variety needs to be based on a robust evaluation of agronomical and physical-chemical characteristics. Here, we characterize new banana varieties resistant to black-Sigatoka leaf spot and compare them to the most widely used traditional variety (Grand Naine). Each variety was evaluated for a range of physic-chemical attributes associated with industrial processing and flavor: pH, TTA, TSS/TTA, total sugars, reducing sugars

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Linero-Ramos, Rafael, Carlos Parra-Rodríguez, Alexander Espinosa-Valdez, Jorge Gómez-Rojas, and Mario Gongora. "Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques." Drones 8, no. 9 (2024): 503. http://dx.doi.org/10.3390/drones8090503.

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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

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Kumakech, A., HJ Lyngs Jørgensen, R. Edema, and P. Okori. "Efficient screening procedure for black sigatoka disease of banana." African Crop Science Journal 23, no. 4 (2015): 387. http://dx.doi.org/10.4314/acsj.v23i4.8.

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Ploetz, R. C. "First Report of Black Sigatoka of Banana Caused by Mycosphaerella fijiensis on Grand Bahama Island." Plant Disease 88, no. 7 (2004): 772. http://dx.doi.org/10.1094/pdis.2004.88.7.772c.

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Black Sigatoka, which is also known as black leaf streak, is caused by Mycosphaerella fijiensis (anamorph Pseudocercospora [formerly Paracercospora) fijiensis]). It is the most important disease of commercially produced banana (Musa spp.) and also has a major impact on production for local consumption. Although the disease occurs throughout the humid tropics, it has been reported in the Caribbean from only Cuba, Hispaniola, and Jamaica (1). In February 2004, black Sigatoka was observed at two isolated and widely separated sites on Grand Bahama island (26.7°N, 78.5°W and 26.7°N, 78°W) on cvs. S

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Washington, J. R., J. Cruz, F. Lopez, and M. Fajardo. "Infection Studies of Mycosphaerella fijiensis on Banana and the Control of Black Sigatoka with Chlorothalonil." Plant Disease 82, no. 11 (1998): 1185–90. http://dx.doi.org/10.1094/pdis.1998.82.11.1185.

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Infection studies with Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana (Musa AAA), demonstrated that the abaxial leaf surface is the primary infection site. Inoculation of banana plants with M. fijiensis ascospores on the abaxial surface of young leaves resulted in disease symptoms in 100% of the leaves inoculated within 18 to 30 days; whereas only 5% of the leaves inoculated on the adaxial surface showed black Sigatoka symptoms within 10 weeks. Disease symptoms appeared more rapidly on the new, emerging leaves than on the first and second fully expanded leaves. Appl

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Alanazi, Rakan. "A YOLOv10-based Approach for Banana Leaf Disease Detection." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23522–26. https://doi.org/10.48084/etasr.11138.

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Leaf disease detection plays a crucial role in modern agricultural management, enabling early intervention to minimize crop losses. This paper explores the application of the YOLOv10 model for detecting and classifying banana leaf conditions with high accuracy. A publicly available dataset of 938 images was used, categorized into five classes, namely Black-Sigatoka, Healthy-Leaf, Panama-Disease, Potassium-Deficiency, and Yellow-Sigatoka. The model achieved a mean Average Precision (mAP@0.5) of 88.85%, a precision of 91.22%, and a recall of 85.06%, demonstrating strong detection capabilities. T

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Yonow, Tania, Julian Ramirez-Villegas, Catherine Abadie, Ross E. Darnell, Noboru Ota, and Darren J. Kriticos. "Black Sigatoka in bananas: Ecoclimatic suitability and disease pressure assessments." PLOS ONE 14, no. 8 (2019): e0220601. http://dx.doi.org/10.1371/journal.pone.0220601.

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SMITH, M. C., J. RUTTER, P. J. A. BURT, F. RAMIREZ, and E. H. GONZALEZO. "Black Sigatoka disease of banana: spatial and temporal variability in disease development." Annals of Applied Biology 131, no. 1 (1997): 63–77. http://dx.doi.org/10.1111/j.1744-7348.1997.tb05396.x.

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martial, KASSI Koffi Fernand Jean, KOUAME Koffi Gaston, KOUAME Konan Didier, et al. "Management of Banana Black Sigatoka in Industrial Dessert Banana Cultivation through the Reasoned Use of Synthetic Fungicide VONDOZEB 62 OD." Scholars Academic Journal of Biosciences 9, no. 11 (2021): 346–53. http://dx.doi.org/10.36347/sajb.2021.v09i11.004.

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Black Sigatoka, caused by the fungus Mycosphaerella fijiensis, is the main devastating banana (Musa sp.) leaf disease. The environmental and health constraints linked to the misuse of chemicals against this disease imply efficient methods aimed at reducing these products. This study, conducted on the Akressi site of Société Agricole Kablan Joublin (SAKJ), an industrial company of dessert banana, assessed the reduction in the dose of contact fungicide associated with adjuvants on black Sigatoka prevalence in Ivorian industrial banana tree plantations. This study made it possible to assess certa

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Nokoe, Sagary, and Rodomiro Ortiz. "Optimum Plot Size for Banana Trials." HortScience 33, no. 1 (1998): 130–32. http://dx.doi.org/10.21273/hortsci.33.1.0130.

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Bananas (Musa spp. AAA group) are an important food and a source of income for farmers in tropical countries. Black sigatoka (Mycosphaerella fijiensis Morelet), a fungal leaf spot disease, is the major production constraint worldwide. The replacement of most of the susceptible banana landraces requires the proper assessment of host plant response to black sigatoka and the determination of yield potential (per unit area and time) of the new selections. Although optimum plot sizes have been reported earlier for banana trials, the number of plants per plot was determined primarily by using a subj

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Strobl, Eric, and Preeya Mohan. "Climate and the Global Spread and Impact of Bananas’ Black Leaf Sigatoka Disease." Atmosphere 11, no. 9 (2020): 947. http://dx.doi.org/10.3390/atmos11090947.

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While Black Sigatoka Leaf Disease (Mycosphaerella fijiensis) has arguably been the most important pathogen affecting the banana industry over the past 50 years, there are no quantitative estimates of what risk factors determine its spread across the globe, nor how its spread has affected banana producing countries. This study empirically models the disease spread across and its impact within countries using historical spread timelines, biophysical models, local climate data, and country level agricultural data. To model the global spread a empirical hazard model is employed. The results show t

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Kumakech, Alfred, Hans J. L. Jørgensen, David B. Collinge, Richard Edema, and Patrick Okori. "Azadirachta indica Reduces Black Sigatoka in East African Highland Banana by Direct Antimicrobial Effects against Mycosphaerella fijiensis without Inducing Resistance." Journal of Agricultural Science 9, no. 4 (2017): 61. http://dx.doi.org/10.5539/jas.v9n4p61.

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Black Sigatoka is a major disease of East African highland cooking bananas in Uganda. Aqueous extracts of Azadirachta indica, Cinnamomum zeylanicum and Capsicum annuum have shown the potential to reduce Black Sigatoka in banana plantlets. The mechanisms by which plant extracts confer protection against plant pathogens has previously been reported to involve activation of defence and direct antimicrobial activity. In the current study, both antimicrobial activities of selected extracts were studied as well as expression of three defence-related genes using quantitative real-time PCR. Gene expre

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Agouanet, Franklin Platini, Israël Tankam-Chedjou, Remy M. Etoua, and Jean Jules Tewa. "Mathematical modelling of Banana Black Sigatoka Disease with delay and Seasonality." Applied Mathematical Modelling 99 (November 2021): 380–99. http://dx.doi.org/10.1016/j.apm.2021.06.030.

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Henderson, J., J. A. Pattemore, S. C. Porchun, et al. "Black Sigatoka disease: new technologies to strengthen eradication strategies in Australia." Australasian Plant Pathology 35, no. 2 (2006): 181. http://dx.doi.org/10.1071/ap06017.

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Canacuán Melo, Flor Yuranny, Yohana Katerine Suárez Anaya, Vicente Emilio Rey Valenzuela, Juan Gonzalo Morales Osorio, and Rafael Eduardo Arango Isaza. "RNA-mediated gene silencing can reduce azole resistance, growth and pathogenicity in Pseudocercospora fijiensis." PLOS One 20, no. 6 (2025): e0325057. https://doi.org/10.1371/journal.pone.0325057.

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The use of double-stranded RNA (dsRNA) gene silencing has allowed significant advances in the understanding of gene function in many organisms and has the potential for use in disease control. At present, there are no adequate methods to induce gene silencing in Pseudocercospora fijiensis (synonym Mycosphaerella fijiensis), the causal agent of black sigatoka, the most economically important disease affecting banana and plantain production. In this work, we developed methods for effective transient gene silencing in both conidial and mycelial cells of P. fijiensis. The results revealed that tre

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Oliveira, Tamiris Y. K., Tatiane C. Silva, Silvino I. Moreira, et al. "Evidence of Resistance to QoI Fungicides in Contemporary Populations of Mycosphaerella fijiensis, M. musicola and M. thailandica from Banana Plantations in Southeastern Brazil." Agronomy 12, no. 12 (2022): 2952. http://dx.doi.org/10.3390/agronomy12122952.

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Yellow and black Sigatoka, caused by Mycosphaerella fijiensis and M. musicola, respectively, are the most important worldwide foliar diseases of bananas. Disease control is heavily dependent on intensive fungicide sprays, which increase selection pressure for fungicide resistance in pathogen populations. The primary objective of this study was to assess the level and spread of resistance to quinone-outside inhibitors (QoI—strobilurin) fungicides in populations of both pathogens sampled from banana fields under different fungicide spray regimes in Southeastern Brazil. Secondly, we aimed to inve

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Juárez Nima, Euder Javier, and Pedro Saul Castillo Carrillo. "Efecto del silicio en el rendimiento de banano (Musa spp.) y control de sigatoka negra." Manglar 21, no. 2 (2024): 237–45. http://dx.doi.org/10.57188/manglar.2024.025.

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In Peru, the plantain or banana is the most important cultivated fruit. In Tumbes 5,000 hectares are cultivated, part of these areas is found in the Zarumilla valley. One of the phytosanitary problems is the black sigatoka (Micosphaerella fijiensis Morelet), which decreases the leaf area of the plantations, with effects on yields. The objective was to evaluate the effect of three doses of Silicon on the yield and control parameters of the aforementioned disease in Musa spp., under field conditions, in said valley. The experimental design used was completely randomized blocks (BCA). According t

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Ioos, R., J. Hubert, C. Abadie, D. Duféal, G. Opdebeeck, and J. Iotti. "First Report of Black Sigatoka Disease in Banana Caused by Mycosphaerella fijiensis on Martinique Island." Plant Disease 95, no. 3 (2011): 359. http://dx.doi.org/10.1094/pdis-11-10-0850.

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Mycosphaerella fijiensis Morelet (anamorph Pseudocercospora fijiensis Morelet), the causal agent of black Sigatoka disease of banana, is considered to be the greatest economical threat for export-banana cultivation throughout the world because most cultivars are highly susceptible. The disease has a worldwide distribution throughout the humid tropical regions, but was still absent in some Caribbean islands hitherto. In Martinique Island, an intensive survey has been conducted by the plant protection service and the Fédération Régionale de Défense Contre les Organismes Nuisibles (FREDON) since

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Jiménez, Nixon, Stefany Orellana, Bertha Mazon-Olivo, Wilmer Rivas-Asanza, and Iván Ramírez-Morales. "Detection of Leaf Diseases in Banana Crops Using Deep Learning Techniques." AI 6, no. 3 (2025): 61. https://doi.org/10.3390/ai6030061.

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Leaf diseases, such as Black Sigatoka and Cordana, represent a growing threat to banana crops in Ecuador. These diseases spread rapidly, impacting both leaf and fruit quality. Early detection is crucial for effective control measures. Recently, deep learning has proven to be a powerful tool in agriculture, enabling more accurate analysis and identification of crop diseases. This study applied the CRISP-DM methodology, consisting of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. A dataset of 900 banana leaf images was collected—30

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Jimenez, Maria, Lieselot Van der Veken, Heleen Neirynck, Helga Rodríguez, Omar Ruiz, and Rony Swennen. "Organic banana production in Ecuador: Its implications on black Sigatoka development and plant–soil nutritional status." Renewable Agriculture and Food Systems 22, no. 4 (2007): 297–306. http://dx.doi.org/10.1017/s1742170507001895.

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AbstractBlack Sigatoka, caused by the leaf fungus Mycosphaerella fijiensis Morelet, is a major constraint to banana production around the world. In Ecuador, the biggest banana-exporting country in the world, this disease has become increasingly aggressive. This has resulted in more fungicide applications, which have significantly increased costs in production and for the environment. Consequently, many banana growers have shifted to organic production, which produces greater economic returns as a result of higher sale prices. In addition, production costs are lower as no fungicides are applied

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Fortune, M. P., S. Gosine, S. Chow, et al. "First report of black sigatoka disease (causal agent Mycosphaerella fijiensis) from Trinidad." Plant Pathology 54, no. 2 (2005): 246. http://dx.doi.org/10.1111/j.1365-3059.2005.01123.x.

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Ortiz, R., and D. Vuylsteke. "Inheritance of black sigatoka disease resistance in plantain-banana (Musa spp.) hybrids." Theoretical and Applied Genetics 89-89, no. 2-3 (1994): 146–52. http://dx.doi.org/10.1007/bf00225134.

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Soares, Julianna Matos da Silva, Anelita de Jesus Rocha, Fernanda dos Santos Nascimento, et al. "Gene Expression, Histology and Histochemistry in the Interaction between Musa sp. and Pseudocercospora fijiensis." Plants 11, no. 15 (2022): 1953. http://dx.doi.org/10.3390/plants11151953.

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Bananas are the main fruits responsible for feeding more than 500 million people in tropical and subtropical countries. Black Sigatoka, caused by the fungus Pseudocercospora fijiensis, is one of the most destructive disease for the crop. This fungus is mainly controlled with the use of fungicides; however, in addition to being harmful to human health, they are associated with a high cost. The development of resistant cultivars through crosses of susceptible commercial cultivars is one of the main focuses of banana breeding programs worldwide. Thus, the objective of the present study was to inv

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Luna-Moreno, Donato, Araceli Sánchez-Álvarez, Ignacio Islas-Flores, et al. "Early Detection of the Fungal Banana Black Sigatoka Pathogen Pseudocercospora fijiensis by an SPR Immunosensor Method." Sensors 19, no. 3 (2019): 465. http://dx.doi.org/10.3390/s19030465.

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Black Sigatoka is a disease that occurs in banana plantations worldwide. This disease is caused by the hemibiotrophic fungus Pseudocercospora fijiensis, whose infection results in a significant reduction in both product quality and yield. Therefore, detection and identification in the early stages of this pathogen in plants could help minimize losses, as well as prevent the spread of the disease to neighboring cultures. To achieve this, a highly sensitive SPR immunosensor was developed to detect P. fijiensis in real samples of leaf extracts in early stages of the disease. A polyclonal antibody

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da Silva, Abimael Gomes, Tatiane Carla Silva, Silvino Intra Moreira, et al. "Resistance to Triazoles in Populations of Mycosphaerella fijiensis and M. musicola from the Sigatoka Disease Complex from Commercial Banana Plantations in Minas Gerais and São Paulo, Brazil." Microorganisms 13, no. 7 (2025): 1439. https://doi.org/10.3390/microorganisms13071439.

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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 tria

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Gomes, Lahyre Izaete S., Greg W. Douhan, Líllian B. J. Bibiano, Luiz A. Maffia, and Eduardo S. G. Mizubuti. "Mycosphaerella musicola Identified as the Only Pathogen of the Sigatoka Disease Complex Present in Minas Gerais State, Brazil." Plant Disease 97, no. 12 (2013): 1537–43. http://dx.doi.org/10.1094/pdis-12-12-1212-re.

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A thorough assessment of the distribution of Mycosphaerella spp. associated with banana in Minas Gerais State, Brazil, was conducted after Mycosphaerella fijiensis was first reported to occur in this region in 2005. From 2009 to 2011, 80 fields located in 20 municipalities including the same fields where the disease was first reported were sampled. A total of 800 samples of leaf tissue with symptoms similar to those of yellow or black Sigatoka diseases were examined, and 239 isolates were obtained. The identification of the fungi was based on morphological characters combined with DNA sequence

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