Bacterial diseases of banana are a growing global threat, causing yield losses and increased management costs. Major diseases include Moko, banana blood disease (BBD), and banana Xanthomonas wilt (BXW), caused by Ralstonia solanacearum, Ralstonia syzygii subsp. celebesensis, and Xanthomonas vasicola pv. musacearum, respectively. Effective surveillance requires point-of-care diagnostics such as loop-mediated isothermal amplification (LAMP) for on-site use. We aimed to develop three LAMP assays to specifically detect the bacteria responsible for Moko, BBD and BXW, directly from banana tissues, using a simplified DNA extraction protocol. The BBD - and BXW-LAMP assays demonstrated 100% specificity, yielding negative results for a broad range of non-target bacteria, including closely related species as well as pathogenic and endophytic strains associated with banana, and positive results for all the tested target strains. For Moko disease, a duplex-LAMP assay was developed to detect all strains from the four globally most relevant sequevars: IIB-3, IIB-4, IIA-6, and IIA-24. The duplex-LAMP successfully detected all target strains, except one that was shown to be non-pathogenic to Cavendish bananas. All non-target strains tested negative, with the exception of a delayed signal for one strain belonging to Ralstonia thomasi, not associated with banana environment (hospital strain). These results were supported by an extensive in silico analysis conducted on 9,668 Burkholderiaceae and 7,483 Xanthomonadaceae genomes. Detection limits ranged from 0.1 pg/µl to 1 pg/µl DNA, and from 104 to 105 CFU/ml on banana tissues spiked with calibrated bacterial suspensions, depending on the assay. The LAMP assays prove highly effective for detecting target pathogens in both artificially inoculated banana plants and field samples, offering a promising tool for improving disease management strategies.

Accurately assessing weevil damage is critical when evaluating banana germplasm. This enables identification of genotypes resistant to the banana weevil, Cosmopolites sordidus , for use as elite parents or for advancement in banana breeding programs. Although visual observation remains the most common phenotyping approach, it is limited by individual bias. This study investigated the potential of image analyses as precise and objective alternatives for assessing weevil damage on the banana corm. ImageJ and four machine learning models, to include YOLO_v11 medium, U-Net 512, SegFormer, and MiT-b0, were explored. 260, 65, and 72 images were used to train, test, and validate the machine learning models, respectively. Phenotyping trials were set up as partially replicated (P-rep) designs with 18 test genotypes and four control genotypes. All plants were produced through tissue culture, raised in pots before infestation with the weevils. At termination, the percentage score of each of the 370 corm samples was evaluated both visually and by image analysis to compare scores across all methods. A significant genotype effect was detected, indicating differences among genotypes in resistance to banana weevils. Furthermore, a significant genotype-by-scoring-method interaction showed that genotype rankings in terms of resistance to banana weevils varied across methods. This emphasized that the choice of scoring approach can affect the magnitude of damage quantified. Visual observation agreed more closely with image analyses for smaller scores and less for larger scores. Results from genotype performance evaluation showed that machine learning methods, except for the YOLO_v11, have a strong level of agreement and can be used interchangeably, giving consistent, reliable, and repeatable measurements. We recommend and have adopted machine learning when scoring weevil damage in the banana corm to avoid individual bias and subjectivity arising from visual observation.

CONTEXT: Tropical agricultural systems must respond to current and future pathogen and pest communities. An important research gap is how climate change may shift the geographic distribution of tropical pathogens and pests. OBJECTIVE: We evaluated the geographic risk of 27 pathogens and pests in four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and potential future risk under climate change. We analyzed model performance for each pathogen and pest, assessing the potential for changes in geographic distribution, and for decision support systems to facilitate management. METHODS: Cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests. We surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying associations. We used machine learning to model each species as a function of environmental variables, including host landscape. We also evaluated future temperatures across altitudes under climate change scenarios. RESULTS AND CONCLUSIONS: Among ten algorithms evaluated, random forests and support vector machines generally performed best for predicting severity or infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44 % of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17 % may become less common. SIGNIFICANCE: These findings indicate how crop health in the region requires adaptation to multiple sustain-ability challenges. The results also indicate which pathogen and pest species have the potential for development of decision support models.

Natural and human-driven disasters are a significant challenge to the sustainable production of food security crops in the Great Lakes region of Africa. A relevant research gap is the effect of climate change on the distribution of pathogens and pests of food security crops in this region. We evaluated the current geographic risk of pathogens and pests in the production of four food security crops - banana, cassava, potato, and sweetpotato - in the region, and the potential future risk under climate change. First, cropland connectivity analysis identified locations likely important in the spread and establishment of crop-specific pathogens and pests for each crop, with locations in Rwanda and Burundi emerging as important across crops. Second, we surveyed 27 economically important pathogens and pests in Rwanda and Burundi, mapping their distribution across landscapes and quantifying patterns of association. Cropland density, cropland connectivity, altitude, and contemporary and long-term temperature and precipitation were strongly associated with disease severity and pest infestation. Among ten machine-learning algorithms evaluated, random forests and support vector machines generally performed best for predicting severity and infestation. Third, an increase in temperature across altitudes is projected under future climate change scenarios in this region. We found evidence that 44% of the pathogen and pest species we studied in banana, potato, and sweetpotato could become more common with warmer temperatures at higher altitudes, while 17% may become less common. Ongoing development of pathogen and pest forecasts can guide strategic surveillance, mitigation and adaptation to future epidemics and pest invasions in high-risk crop locations under climate change. ### Competing Interest Statement The authors have declared no competing interest.

The burrowing nematode, Radopholus similis, is considered the most important nematode pest threatening banana production worldwide. Among the various nematode management strategies available, host resistance is one of the most promising. However, there is little information on host resistance in bananas against nematodes, including R. similis, which is partly explained by the inconsistency of the results and the resistance status of cultivars. To reduce such inconsistency, it would be beneficial to use a standardized detection protocol, utilizing appropriate planting substrates that allow the penetration and reproduction of nematodes without interfering with plant growth. Sterilized forest soil has commonly been used, but it limits the penetration of nematodes into the host roots due to its fine texture. Consequently, a more suitable substrate could improve the efficiency of screening for host resistance of bananas against nematodes. In this study, R. similis was used as a model nematode to evaluate cocopeat and river sand as alternative substrates to forest soil that is normally used in pot evaluations. In the pots, in vitro plants of the cultivars Yangambi Km5 and Grande Naine were planted, representing resistant and susceptible genotypes, respectively. The findings revealed that different substrates showed a differential impact on the growth parameters of the plants (underground biomass), allowing the penetration and reproduction of R. similis within the roots of banana plants. A high growth of plants in sterilized forest soil was observed, followed by cocopeat, and then river sand. In contrast, plants grown in river sand showed a higher number of R. similis in the roots and a greater reproduction rate, compared to those grown in cocopeat and sterilized forest soil. Although cocopeat favored plant growth, it hindered the penetration of R. similis and reduced reproduction rates. Moreover, there was a high proportion of root loss in the harvest due to the strong adherence of cocopeat to the roots. Therefore, it is suggested to use river sand as the most appropriate substrate to evaluate the response of banana genotypes to R. similis infection under greenhouse conditions. This would improve the efficiency of resistance evaluations of bananas against nematodes.

Bacterial diseases of banana are becoming increasingly significant worldwide, resulting in reduced yields and higher disease management costs. The most important bacterial diseases of banana include Moko and banana blood disease (BBD), caused by Ralstonia solanacearum and Ralstonia syzygii subsp. celebesensis , respectively, and banana Xanthomonas wilt (BXW) caused by Xanthomonas vasicola pv. musacearum . Effective surveillance and disease management require point-of-care diagnostics, such as loop-mediated isothermal amplification (LAMP), for on-site operation. In this study, three LAMP assays were developed to specifically detect the bacteria responsible for Moko, BBD and BXW, directly from banana tissues, using a simplified DNA extraction protocol. The BBD - and BXW-LAMP assays demonstrated 100% specificity, yielding negative results for a broad range of non-target bacteria, including closely related species as well as pathogenic and endophytic strains associated with banana, and positive results for all the tested target strains. For Moko disease, a duplex-LAMP assay was developed to detect all strains from the four globally most relevant sequevars: IIB-3, IIB-4, IIA-6, and IIA-24. The duplex-LAMP successfully detected all target strains, except one that was shown to be non-pathogenic to Cavendish bananas. All non-target strains tested negative, with the exception of a delayed signal for one strain belonging to Ralstonia thomasi , not associated with banana environment (hospital strain). These results were supported by an extensive in silico analysis conducted on 9,668 Burkholderiaceae and 7,483 Xanthomonadaceae genomes. Detection limits ranged from 0.1 pg/µl to 1 pg/µl DNA, and from 10⁴ to 10⁵ CFU/ml on banana tissues spiked with calibrated bacterial suspensions, depending on the assay. The LAMP assays prove highly effective for detecting target pathogens in both artificially inoculated banana plants and field samples, offering a promising tool for improving disease management strategies. ### Competing Interest Statement The authors have declared no competing interest.

Banana Xanthomonas wilt, caused by Xanthomonas vasicola pv. musacearum (Xvm), is a devastating disease that results in total yield loss of affected plants. Resistance to the disease is limited in Musa acuminata, but it has been identified so far in the zebrina subspecies. This study identified markers associated with tolerance to Xvm in Monyet, a tetraploid banana from the zebrina subspecies which was identified to be partially resistant to the bacterium. We used a triploid progeny of 135 F1 hybrids resulting from a cross between Monyet (Xvm partially resistant) and Kokopo (diploid and Xvm susceptible). The F1 hybrids were screened in pots for resistance to Xvm. The population was genotyped using the genotyping-by-sequencing platform of Diversity Array Technology (DArTSeq). The adjusted means of the phenotypic data were combined with the allele frequencies of the genotypic data in continuous mapping. We identified 25 SNPs associated with resistance to Xvm, and these were grouped into five quantitative traits loci (QTL) on chromosomes 2, 3, 6, and 7. For each marker, we identified the favorable allele and the additive effect of replacing the reference allele with the alternative allele. The comparison between weevil borer (Cosmopolites sordidus (Germar)) and Xvm QTL revealed one QTL shared between the two biotic stresses at the distal end of chromosome 6 but with a repulsion linkage. This linkage should be broken down by generating more recombinants in the region. We also identified 18 putative alleles in the vicinity of the SNPs associated with resistance to Xvm. Among the 18 putative genes, two particularly putative genes, namely, Ma06_g13550 and Ma06_g36840, are most likely linked to disease resistance. This study is a basis for marker-assisted selection to improve banana resistance to banana Xanthomonas wilt, especially in East and Central Africa where the disease is still devastating the crop.

Accurately assessing weevil damage is critical when evaluating banana germplasm to identify genotypes resistant to the banana weevil (Cosmopolites sordidus), for use as elite parents in the banana breeding pipeline or evaluating breeding products. Visual observation remains the most common phenotyping approach but limited by individual bias. This study investigated the potential of image analyses as precise and objective alternatives for assessing weevil damage on the banana corm. Phenotyping trials were set up as partially replicated (P-rep) designs with 22 tissue culture-generated genotypes raised in pots and infested with banana weevils. At termination, the percentage score of weevil damage to the corms was evaluated by visual observation and image analysis using ImageJ and machine learning. In total, 370 high-quality images were assessed for weevil damage using ImageJ and machine learning. On average, damage scores from visual observation were 5.52% and 3.88% higher than ImageJ and machine learning respectively. There was a proportional trend with visual observation agreeing closely to image analyses for smaller scores and but less for larger scores. The results show that both ImageJ and machine learning exhibited a strong level of agreement and are interchangeable with consistent, reliable, and repeatable measurements. In conclusion, to avoid individual bias and subjectivity arising from visual observation, we recommend the use of either ImageJ or machine learning when scoring weevil damage in the banana corm.

Pests and diseases are key biotic constraints limiting banana production among smallholder farmers in Eastern and Central Africa. Climate changemay favour pest and disease development and further exacerbate the vulnerability of smallholder farming systems to biotic constraints. Information on effects of climate change on pests and pathogens of banana is required by policy makers and researchers in designing control strategies and adaptation plans. Since altitude is inversely related to temperature, this study used the occurrence of key banana pests and diseases along an altitude gradient as a proxy for the potential impact of changes in temperature associated with global warming on pests and diseases. We assessed the occurrence of banana pests and diseases in 93 banana fields across three altitude ranges in Burundi and 99 fields distributed in two altitude ranges in Rwanda watersheds. Incidence and prevalence of Banana Bunchy Top Disease (BBTD) and Fusarium wilt (FW) was significantly associated with temperature and altitude in Burundi, revealing that increasing temperatures may lead to upward movement of banana diseases. No significant associations with temperature and altitude were observed for weevils, nematodes and Xanthomonas wilt of banana (BXW). Data collected in this study provides a baseline to verify and guide modelling work to predict future pest and disease distribution according to climate change scenarios. Such information is useful in informing policy makers and designing appropriate management strategies.

[This corrects the article DOI: 10.1016/j.heliyon.2018.e01080.].

For decades, Xanthomonas vasicola pv. musacearum (Xvm) has been an economically important bacterial pathogen on enset in Ethiopia. Since 2001, Xvm has also been responsible for significant losses to banana crops in several East and Central African countries, with devastating consequences for smallholder farmers. Understanding the genetic diversity within Xvm populations is essential for the smart design of transnationally reasoned, durable, and effective management practices. Previous studies have revealed limited genetic diversity in Xvm, with East African isolates from banana each falling into one of two closely related clades previously designated as sublineages SL 1 and SL 2, the former of which had also been detected on banana and enset in Ethiopia. Given the presumed origin of Xvm in Ethiopia, we hypothesized that both clades might be found in that country, along with additional genotypes not seen in Central and East African bananas. Genotyping of 97 isolates and whole-genome sequencing of 15 isolates revealed not only the presence of SL 2 in Ethiopia, but additional diversity beyond SL 1 and SL 2 in four new clades. Moreover, SL 2 was detected in the Democratic Republic of Congo, where previously SL 1 was the only clade reported. These results demonstrate a greater range of genetic diversity among Xvm isolates than previously reported, especially in Ethiopia, and further support the hypothesis that the East/Central Africa xanthomonas wilt epidemic has been caused by a restricted set of genotypes drawn from a highly diverse pathogen pool in Ethiopia.

We present an amended description of the bacterial species Xanthomonas vasicola to include the causative agent of banana Xanthomonas wilt, as well as strains that cause disease on Areca palm, Tripsacum grass, sugarcane, and maize. Genome-sequence data reveal that these strains all share more than 98% average nucleotide with each other and with the type strain. Our analyses and proposals should help to resolve the taxonomic confusion that surrounds some of these pathogens and help to prevent future use of invalid names.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Xanthomonas vasicola pv. musacearum (Xvm) which causes Xanthomonas wilt (XW) on banana (Musa accuminata x balbisiana) and enset (Ensete ventricosum), is closely related to the species Xanthomonas vasicola that contains the pathovars vasculorum (Xvv) and holcicola (Xvh), respectively pathogenic to sugarcane and sorghum. Xvm is considered a monomorphic bacterium whose intra-pathovar diversity remains poorly understood. With the sudden emergence of Xvm within east and central Africa coupled with the unknown origin of one of the two sublineages suggested for Xvm, attention has shifted to adapting technologies that focus on identifying the origin and distribution of the genetic diversity within this pathogen. Although microbiological and conventional molecular diagnostics have been useful in pathogen identification. Recent advances have ushered in an era of genomic epidemiology that aids in characterizing monomorphic pathogens. To unravel the origin and pathways of the recent emergence of XW in Eastern and Central Africa, there was a need for a genotyping tool adapted for molecular epidemiology. Multi-Locus Variable Number of Tandem Repeat Analysis (MLVA) is able to resolve the evolutionary patterns and invasion routes of a pathogen. In this study, we identified microsatellite loci from nine published Xvm genome sequences. Of the 36 detected microsatellite loci, 21 were selected for primer design and 19 determined to be highly typeable, specific, reproducible and polymorphic with two- to four- alleles per locus on a sub-collection. The 19 markers were multiplexed and applied to genotype 335 Xvm strains isolated from seven countries over several years. The microsatellite markers grouped the Xvm collection into three clusters; with two similar to the SNP-based sublineages 1 and 2 and a new cluster 3, revealing an unknown diversity in Ethiopia. Five of the 19 markers had alleles present in both Xvm and Xanthomonas vasicola pathovars holcicola and vasculorum, supporting the phylogenetic closeliness of these three pathovars. Thank to the public availability of the haplotypes on the MLVABank database, this highly reliable and polymorphic genotyping tool can be further used in a transnational surveillance network to monitor the spread and evolution of XW throughout Africa.. It will inform and guide management of Xvm both in banana-based and enset-based cropping systems. Due to the suitability of MLVA-19 markers for population genetic analyses, this genotyping tool will also be used in future microevolution studies.

Alternative host plants are important in the survival and perpetuation of several crop pathogens and have been suspected to play a role in the survival of Xanthomonas campestris pv. musacearum (Xcm) and perpetuation of Xanthomonas wilt (XW) disease of banana and enset. This study determined the potential risk posed by two weeds (Canna spp. and wild sorghum) and common banana intercrops (maize, millet, sorghum, taro, and sugarcane) as alternative hosts to Xcm. The study employed screenhouse experiments, laboratory procedures and diagnosis of banana fields in XW-affected landscapes. Typical XW symptoms were only observed in artificially inoculated Canna sp., with an incidence of 96%. Leaf lesions characteristic of xanthomonads occurred on millet (50%) and sorghum (35%), though the plants recovered. No symptoms occurred in maize, sugarcane, taro or wild sorghum. However, Xcm was recovered from all these plant species, with higher recoveries in Canna sp. (47%), millet (27%), sugarcane (27%), and wild sorghum (25%). Only isolates recovered from Canna sp., millet, sorghum and wild sorghum caused disease in banana plantlets. The presence and incidence of XW on-farm was positively associated with the presence of susceptible ABB Musa genotypes and negatively with number of banana cultivars on farm and household access to training on XW management. Only 0.02% of field sampled Canna spp. plants had Xcm. Risk posed by Canna spp. on-farm could be limited to tool transmission as it has persistent floral bracts that prevent insect-mediated infections. Given the high susceptibility, perennial nature and propagation through rhizomes of Canna sp., it could pose a moderate-high risk, thus warranting some attention in the management of XW disease. Sugarcane could offer a low-moderate risk due to its perennial nature and propagation through rhizomes while risk from maize, millet, and sorghum was deemed zero-low due to their annual nature, wind-mediated mode of pollination and propagation through seed. Understanding the interactions of a crop pathogen with other plants is thus important when diversifying agroecosystems. The study findings also suggest other factors such as cultivar composition and management of the disease at farm and landscape level to be important in the perpetuation of XW disease.

Xanthomonas vasicola pv. musacearum (Xvm) is a bacterial pathogen responsible for the economically important Xanthomonas wilt disease on banana and enset crops in Sub-Saharan Africa. Given that the symptoms are similar to those of other diseases, molecular diagnosis is essential to unambiguously identify this pathogen and distinguish it from closely related strains not pathogenic on these hosts. Currently, Xvm identification is based on polymerase chain reaction (PCR) with GspDm primers, targeting the gene encoding general secretory protein D. Experimental results and examination of genomic sequences revealed poor .e01080 vier Ltd. This is an open access article under the CC BY-NC-ND license y-nc-nd/4.0/). 2 https://doi.org/10.1016/j.heliy 2405-8440/ 2018 Published (http://creativecommons.org/li Article Nowe01080 specificity of the GspDm PCR. Here, we present and validate five new Xvmspecific primers amplifying only Xvm strains.

Xanthomonas vasicola pv. musacearum (Xvm) is a bacterial pathogen responsible for the economically important Xanthomonas wilt disease on banana and enset crops in Sub-Saharan Africa. Given that the symptoms are similar to those of other diseases, molecular diagnosis is essential to unambiguously identify this pathogen and distinguish it from closely related strains not pathogenic on these hosts. Currently, Xvm identification is based on polymerase chain reaction (PCR) with GspDm primers, targeting the gene encoding general secretory protein D. Experimental results and examination of genomic sequences revealed poor specificity of the GspDm PCR. Here, we present and validate five new Xvm-specific primers amplifying only Xvm strains.

TAXONOMY:Bacteria; Phylum Proteobacteria; Class Gammaproteobacteria; Order Xanthomonadales; Family Xanthomonadaceae; Genus Xanthomonas; currently classified as X. campestris pv. musacearum (Xcm). However, fatty acid methyl ester analysis and genetic and genomic evidence suggest that this pathogen is X. vasicola and resides in a separate pathovar. ISOLATION AND DETECTION:Xcm can be isolated on yeast extract peptone glucose agar (YPGA), cellobiose cephalexin agar and yeast extract tryptone sucrose agar (YTSA) complemented with 5-fluorouracil, cephalexin and cycloheximide to confer semi-selectivity. Xcm can also be identified using direct antigen coating enzyme-linked immunosorbent assay (DAC-ELISA), species-specific polymerase chain reaction (PCR) using GspDm primers and lateral flow devices that detect latent infections. HOST RANGE:Causes Xanthomonas wilt on plants belonging to the Musaceae, primarily banana (Musa acuminata), plantain (M. acuminata × balbisiana) and enset (Ensete ventricosum). DIVERSITY:There is a high level of genetic homogeneity within Xcm, although genome sequencing has revealed two major sublineages. SYMPTOMS:Yellowing and wilting of leaves, premature fruit ripening and dry rot, bacterial exudate from cut stems. DISTRIBUTION:Xcm has only been found in African countries, namely Burundi, Ethiopia, Democratic Republic of the Congo, Kenya, Rwanda, Tanzania and Uganda. ECOLOGY AND EPIDEMIOLOGY:Xcm is transmitted by insects, bats, birds and farming implements. Long-distance dispersal of the pathogen is by the transportation of latently infected plants into new areas. MANAGEMENT:The management of Xcm has relied on cultural practices that keep the pathogen population at tolerable levels. Biotechnology programmes have been successful in producing resistant banana plants. However, the deployment of such genetic material has not as yet been achieved in farmers' fields, and the sustainability of transgenic resistance remains to be addressed.

Management of banana xanthomonas wilt (XW) (caused by Xanthomonas campestris pv. musacearum, Xcm) has been impeded by poor adoption of control options that are complex, cumbersome and costly. To improve XW management, this study investigated Xcm survival and latent infections in subsequent generations, survival of latently infected planting materials (suckers), incidence of latent infections in symptomless plants in mats having diseased plants, and XW status across farms and markets in districts previously devastated but currently endemic. On‐station experiments were protected from new infections. Latent bacteria at low levels were detected in up to 20% of the third generation suckers, with a significant (P < 0·05) reduction (43–20%) in subsequent generations. Only 3–6% of latently infected suckers succumbed to XW. Incidence of Xcm in symptomless suckers from farmers' fields (with up to 70% incidence) was low (3%) while it increased (8–25%) with disease severity in mats in controlled experiments. In the surveyed districts, incidence had significantly declined with yields observed to have recovered relative to earlier reports, although latent infections remained high. This study provides evidence that if new infections are prevented, fields with high XW incidence can be rejuvenated. It showed incomplete systemic movement of Xcm in mats coupled to a gradual decline of bacterial load in subsequent generations to levels that cannot initiate disease. These studies explain the current successes in farms practising single diseased plant removal instead of whole mat rouging, and gives hope to farmers lacking access to clean planting material.

Xanthomonas campestris pv. musacearum (Xcm) is the causal agent of banana xanthomonas wilt, a major threat to banana production in eastern and central Africa. The pathogen is present in very high levels within infected plants and can be transmitted by a broad range of mechanisms; therefore early specific detection is vital for effective disease management. In this study, a polyclonal antibody (pAb) was developed and deployed in a lateral flow device (LFD) format to allow rapid in‐field detection of Xcm. Published Xcm PCR assays were also independently assessed: only two assays gave specific amplification of Xcm, whilst others cross‐reacted with non‐target Xanthomonas species. Pure cultures of Xcm were used to immunize a rabbit, the IgG antibodies purified from the serum and the resulting polyclonal antibodies tested using ELISA and LFD. Testing against a wide range of bacterial species showed the pAb detected all strains of Xcm, representing isolates from seven countries and the known genetic diversity of Xcm. The pAb also detected the closely related Xanthomonas axonopodis pv. vasculorum (Xav), primarily a sugarcane pathogen. Detection was successful in both naturally and experimentally infected banana plants, and the LFD limit of detection was 105 cells mL−1. Whilst the pAb is not fully specific for Xcm, Xav has never been found in banana. Therefore the LFD can be used as a first‐line screening tool to detect Xcm in the field. Testing by LFD requires no equipment, can be performed by non‐scientists and is cost‐effective. Therefore this LFD provides a vital tool to aid in the management and control of Xcm.