Search Results (43)

Fungicide spraying by remotely piloted aircrafts (RPA) in banana farming is a promising alternative, but the relationship among operating parameters, application quality and biological effectiveness in disease control such as Sigatoka spp. is still poorly understood. The aim of this study was to assess the influence of different application rates and droplet sizes on spraying quality and Sigatoka mitigation in banana plants of the ‘Prata’ cultivar. A field experiment was conducted using an RPA, four rates of application (8, 10, 12 and 14 L ha⁻¹) and three classes of droplet sizes selected on the RPA radio control (180, 240 and 300 μm). The application parameters significantly influenced the coverage and drop deposition. However, under the evaluated experimental conditions, no significant monotonic association was detected between these spray quality indicators and disease control efficacy, as assessed by the Stover Index. Lower rates of application (8 L ha⁻¹) showed comparable disease control to higher rates. The results suggest that the relationship between quantitative deposition and biological efficacy was not directly proportional under the conditions evaluated, and a biological control threshold can be reached even with lower quantitative deposition, making operating efficiency optimization a viable strategy. Full article

The rhizosphere bacterial community is an important determinant of plant health and disease resistance. To investigate bacterial community characteristics associated with Fusarium wilt resistance, we used full-length 16S rRNA amplicon sequencing to characterize rhizosphere bacterial communities. The analysis included seven banana germplasms with contrasting resistance levels. Significant genotype-dependent differences in bacterial community composition were detected among the germplasms. Alpha diversity analysis showed that bacterial richness and diversity were not consistently associated with Fusarium wilt resistance. In contrast, highly resistant germplasms were enriched in potentially beneficial taxa, including Bacillus and Nitrospira, whereas Chujaibacter and Acinetobacter were more abundant in moderately resistant and low-resistant germplasms. Beta diversity analysis further revealed distinct community structures across the germplasms. Functional prediction indicated that bacterial communities associated with highly resistant germplasms possessed greater potential for carbohydrate metabolism, secondary metabolite biosynthesis, and antibiotic biosynthesis. Co-occurrence network analysis revealed distinct bacterial interaction patterns among germplasms with different resistance levels. Highly resistant germplasms showed strong associations among several key bacterial taxa. In contrast, moderately resistant and low-resistant germplasms exhibited different patterns of positive and negative bacterial associations. Collectively, these results suggest that Fusarium wilt resistance in banana is associated not with increased bacterial diversity but with the enrichment of potentially beneficial bacterial taxa, enhanced bacterial functional potential, and distinct bacterial interaction patterns. Full article

Vegetatively propagated crops, including cassava, sweet potato, banana, and potato, are susceptible to mixed-pathogen infections resulting from the continuous use of clonal planting material and infrequent seed replacement. A diverse array of viruses, bacteria, and fungi can accumulate within these materials over successive cultivation cycles, precipitating seed degeneration and complex disease syndromes that complicate diagnosis and management. Mixed infections frequently trigger synergistic interactions that exacerbate disease severity and yield losses. This review synthesizes data on mixed-pathogen complexes in vegetatively propagated crops, with particular focus on vascular and systemically colonizing pathogens and analyzing starch crops to highlight the epidemiological, biological, and ecological drivers of synergism and antagonism. Furthermore, the review examines host defense responses during coinfection, including the modulation of plant immune pathways, and evaluates how interpathogen dynamics influence pathological outcomes. Although advancements in molecular diagnostics—notably next-generation sequencing and metagenomics—have revolutionized the detection of mixed infections, they have also introduced challenges in differentiating causal agents from commensal microorganisms. Finally, we discuss the implications for integrated disease management, emphasizing clean seed systems, resistance breeding, and phenotyping strategies tailored to multipathogen environments. The dynamics of mixed infections is critical for resilient and sustainable management strategies amidst increasingly complex agricultural and climatic shifts. Full article

Banana production is a vital agricultural sector in the Philippines and faces major threats from Bacterial Wilt, Banana Bunchy Top Disease, Sigatoka, and Panama. We developed a portable, non-destructive detection system using image processing and deep learning to classify banana leaf diseases. Using the MobileNetV2 architecture, the system achieved 74.6% accuracy, with the highest performance on Bacterial Wilt (F1 = 0.76) and Healthy leaves (F1 = 0.85), and lower results on Banana Bunchy Top Disease (F1 = 0.50). The system provided severity scoring through Open Source Computer Vision Library segmentation: low (0–20%), moderate (21–40%), and high (>41%). Despite power and thermal constraints, the prototype proved effective for early, field-ready disease diagnosis. Full article

The genera Pestalotiopsis, Neopestalotiopsis, and Pseudopestalotiopsis share highly similar morphological characteristics. Although species within these genera are recognized as plant pathogens, their pathogenicity can differ even on the same host plant, highlighting the importance of accurate genus-level identification for effective disease management. However, reliable discrimination among these genera based solely on morphology or internal transcribed spacer (ITS) amplicon length is difficult. Therefore, molecular approaches based on ITS sequence data are required for practical and reliable genus-level identification. This study aimed to develop genus-specific PCR primers through comparative genome analysis using genus-specific gene regions identified from available genomic data. The performance of these primers was evaluated using 49 isolates obtained from banana, Japanese andromeda, loquat, rubber, and tea. The primer sets achieved an overall identification accuracy of 97%. One strain could not be assigned to Pestalotiopsis, which exhibited morphological characteristics inconsistent with the genus and was positioned outside the main Pestalotiopsis clade in phylogenetic analyses, supporting the taxonomic validity of the primer-based identification. These results demonstrate that the developed primers provide a reliable and practical tool for genus-level identification and taxonomic assignment of these morphologically similar fungal pathogens, including direct detection from infected plant tissues. Full article

This paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2). The system feeds from multispectral images captured by Unmanned Aerial Vehicles (UAVs) alongside data from IoT nodes. The primary objective is to improve classification performance in terms of both accuracy and precision. This is achieved by integrating contemporary Deep Learning techniques, specifically different CNN models, a prevalent type of artificial neural network composed of multiple interconnected layers, tailored for the analysis of agricultural imagery. The initial layers are responsible for identifying basic visual features such as edges and contours, while deeper layers progressively extract more abstract and complex patterns, enabling the recognition of intricate shapes. In this study, different datasets of tropical crop images, in this case banana crops, were constructed to evaluate the performance and accuracy of CNNs in detecting diseases in the crops, supported by transfer learning. For this, multispectral images are used to create false-color images to discriminate disease through spectra related to the blue, green and red colors in addition to red edge and near-infrared. Moreover, we used IoT nodes to include environmental data related to the temperature and humidity of the environment and the soil. Machine Learning models were evaluated and fine-tuned using standard evaluation metrics. For classification, we used fundamental metrics such as accuracy, precision, and the confusion matrix; in this study was obtained a performance of up to 86.5% using current deep learning models and up to 98.5% accuracy using the proposed hybrid and hierarchical architecture (SVMobileNetV2). This represents a new paradigm to significantly improve classification using the proposed hybrid CNN-SVM architecture and UAV-based multispectral images. Full article

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. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

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. Full article

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—300 of Black Sigatoka, 300 of Cordana, and 300 of healthy leaves. Three pre-trained models (EfficientNetB0, ResNet50, and VGG19) were trained on this dataset. To improve performance, data augmentation techniques were applied using TensorFlow Keras’s ImageDataGenerator class, expanding the dataset to 9000 images. Due to the high computational demands of ResNet50 and VGG19, training was performed with EfficientNetB0. The models—EfficientNetB0, ResNet50, and VGG19—demonstrated the ability to identify leaf diseases in bananas, with accuracies of 88.33%, 88.90%, and 87.22%, respectively. The data augmentation increased the performance of EfficientNetB0 to 87.83%, but did not significantly improve its accuracy. These findings highlight the value of deep learning techniques for early disease detection in banana crops, enhancing diagnostic accuracy and efficiency. Full article

Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense (Foc), is recognized as one of the most devastating diseases affecting banana cultivation worldwide. In India, Foc extensively affects Malbhog banana (AAB genomic group) production. In this study, we isolated 25 Foc isolates from wilt-affected Malbhog plantations inIndia. A pathogenicity test confirmed the identity of these isolates as Foc, the primary causative agent of wilt in bananas. The morpho-cultural characterization of Foc isolates showed large variations in colony morphological features, intensity, and pattern of pigmentation, chlamydospores, and conidial size. The molecular identification of these isolates using Race1- and Race4-specific primers established their identity as Race1 of Foc, with the absence of Tropical Race 4 of Foc. For a more comprehensive understanding of the genetic diversity of Foc isolates, we employed ISSR molecular typing, which revealed five major clusters. About 96% of the diversity within the Foc population indicated the presence of polymorphic loci in individuals of a given population evident from the results of Nei’s genetic diversity, Shannon’s information index, and the polymorphism information content values, apart from the analysis of molecular variance (AMOVA). The current findings provide significant insights toward the detection of Foc variants and, consequently, the deployment of effective management practices to keep the possible epidemic development of disease under control along the Malbhog banana growing belts of northeast India. Full article

Dopamine is a neurotransmitter which is classified as a catecholamine. It is also one of the main metabolites produced by some tumor types (such as paragangliomas and neoblastomas). As such, determining and monitoring the level of dopamine is of the utmost importance, ideally using analytical techniques that are sensitive, simple, and low in cost. Due to this, we have developed a non-enzymatic dopamine sensor that is highly sensitive, selective, and rapidly detects the presence of dopamine in the body. A hybrid material fabricated with NiO and ZnO, based on date fruit extract, was synthesized by hydrothermal methods and using NiO as a precursor material. This paper discusses the role of date fruit extracts in improving NiO’s catalytic performance with reference to ZnO and the role that they play in this process. An X-ray powder diffraction study, a scanning electron microscope study, and a Fourier transform infrared spectroscopy study were performed in order to investigate the structure of the samples. It was found that, in the composite NiO/ZnO, NiO exhibited a cubic phase and ZnO exhibited a hexagonal phase, both of which exhibited well-oriented aggregated cluster shapes in the composite. A hybrid material containing NiO and ZnO has been found to be highly electro-catalytically active in the advanced oxidation of dopamine in a phosphate buffer solution at a pH of 7.3. It has been found that this can be accomplished without the use of enzymes, and the range of oxidation used here was between 0.01 mM and 4 mM. The detection limit of non-enzymatic sensors is estimated to be 0.036 μM. Several properties of the non-enzymatic sensor presented here have been demonstrated, including its repeatability, selectivity, and reproducibility. A test was conducted on Sample 2 for the detection of banana peel and wheat grass, and the results were highly encouraging and indicated that biomass waste may be useful for the manufacture of medicines to treat chronic diseases. It is thought that date fruit extracts would prove to be valuable resources for the development of next-generation electrode materials for use in clinical settings, for energy conversion, and for energy storage. Full article

This study characterized an endophytic fungus, DJE2023, isolated from healthy banana sucker of the cultivar (cv.) Dajiao. Its potential as a biocontrol agent against banana Fusarium wilt was assessed, aiming to provide a novel candidate strain for the biological control of the devastating disease. The fungus was isolated using standard plant tissue separation techniques and fungal culture methods, followed by identification through morphological comparisons, multi-gene phylogenetic analyses, and molecular detection targeting Fusarium oxysporum f. sp. cubense (Foc) race 1 and race 4. Furthermore, assessments of its characteristics and antagonistic effects were conducted through pathogenicity tests, biological trait investigations, and dual-culture experiments. The results confirmed isolate DJE2023 to be a member of the Fusarium oxysporum species complex but distinct from Foc race 1 or race 4, exhibiting no pathogenicity to banana plantlets of cv. Fenza No.1 or tomato seedlings cv. money maker. Only minute and brown necrotic spots were observed at the rhizomes of banana plantlets of ‘Dajiao’ and ‘Baxijiao’ upon inoculation, contrasting markedly with the extensive necrosis induced by Foc tropical race 4 strain XJZ2 at those of banana cv Baxijiao. Notably, co-inoculation with DJE2023 and XJZ2 revealed a significantly reduced disease severity compared to inoculation with XJZ2 alone. An in vitro plate confrontation assay showed no significant antagonistic effects against Foc, indicating a suppressive effect rather than direct antagonism of DJE2023. Research on the biological characteristics of DJE2023 indicated lactose as the optimal carbon source for its growth, while maltose favored sporulation. The optimal growth temperature for this strain is 28 °C, and its spores can germinate effectively within the range of 25–45 °C and pH 4–10, demonstrating a strong alkali tolerance. Collectively, our findings suggest that DJE2023 exhibits weak or non-pathogenic properties and lacks direct antagonism against Foc, yet imparts a degree of resistance against banana Fusarium wilt. The detailed information provides valuable insight into the potential role of DJE2023 in integrated banana disease control, presenting a promising candidate for biocontrol against banana Fusarium wilt. Full article

Fusarium wilt of banana is a soil-borne vascular disease caused by Fusarium oxysporum f. sp. cubense. The rapid and accurate detection of this disease is of great significance to controlling its spread. The research objective was to explore rapid banana Fusarium wilt latency and onset detection methods and establish a disease severity grading model. Visible/near-infrared spectroscopy analysis combined with machine learning methods were used for the rapid in vivo detection of banana Fusarium wilt. A portable visible/near-infrared spectrum acquisition system was constructed to collect the spectra data of banana Fusarium wilt leaves representing five different disease grades, totaling 106 leaf samples which were randomly divided into a training set with 80 samples and a test set with 26 samples. Different data preprocessing methods were utilized, and Fisher discriminant analysis (FDA), an extreme learning machine (ELM), and a one-dimensional convolutional neural network (1D-CNN) were used to establish the classification models of the disease grades. The classification accuracies of the FDA, ELM, and 1D-CNN models reached 0.891, 0.989, and 0.904, respectively. The results showed that the proposed visible/near infrared spectroscopy detection method could realize the detection of the incubation period of banana Fusarium wilt and the classification of the disease severity and could be a favorable tool for the field diagnosis of banana Fusarium wilt. Full article

Fusarium wilt is a soil borne fungal disease that has devastated banana production in plantations around the world. Most Cavendish-type bananas are susceptible to strains of Fusarium oxysporum f. sp. cubense (Foc) belonging to the Subtropical Race 4 (STR4) and Tropical Race 4 (TR4). The wild banana diploid Musa acuminata ssp. malaccensis (AA, 2n = 22) carries resistance to Foc TR4. A previous study using segregating populations derived from M. acuminata ssp. malaccensis identified a quantitative trait locus (QTL) (12.9 cM) on the distal part of the long arm of chromosome 3, conferring resistance to both Foc TR4 and STR4. An SNP marker, based on the gene Macma4_03_g32560 of the reference genome ‘DH-Pahang’ v4, detected the segregation of resistance to Foc STR4 and TR4 at this locus. Using this marker, we assessed putative TR4 resistance sources in 123 accessions from the breeding program in Brazil, which houses one of the largest germplasm collections of Musa spp. in the world. The resistance marker allele was detected in a number of accessions, including improved diploids and commercial cultivars. Sequencing further confirmed the identity of the SNP at this locus. Results from the marker screening will assist in developing strategies for pre-breeding Foc TR4-resistant bananas. This study represents the first-ever report of marker-assisted screening in a comprehensive collection of banana accessions in South America. Accessions carrying the resistance marker allele will be validated in the field to confirm Foc TR4 resistance. Full article