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To describe the transmission dynamics of maize streak virus infection, in the paper, we first formulate a stochastic maize streak virus infection model, in which the stochastic fluctuations are depicted by a logarithmic Ornstein–Uhlenbeck process. This approach is reasonable to simulate the random impacts of main parameters both from the biological significance and the mathematical perspective. Then we investigate the detailed dynamics of the stochastic system, including the existence and uniqueness of the global solution, the existence of a stationary distribution, the exponential extinction of the infected maize and infected leafhopper vector. Especially, by solving the five-dimensional algebraic equations corresponding to the stochastic system, we obtain the specific expression of the probability density function near the quasi-endemic equilibrium of the stochastic system, which provides valuable insights into the stationary distribution. Finally, the model is discretized using the Milstein higher-order numerical method to illustrate our theoretical results numerically. Our findings provide a groundwork for better methods of preventing the spread of this type of virus. [ABSTRACT FROM AUTHOR]

Maize is a major staple food in Sub-Saharan Africa (SSA). Vitamin A deficiency index is high in Africa and could be reduced through the consumption of provitamin-A-enriched maize. However, foliar diseases such as maize streak virus, northern corn leaf blight and common rust constrain maize production in SSA. The cultivation of host-resistant varieties is the most effective approach to mitigate their effects. Therefore, maize synthetics improved for PVA carotenoids, their selection cycles and crosses as well as a commercial disease-resistant check were assessed for resistance to maize streak virus, northern corn leaf blight and common rust at hotspots in Nigeria. The foliar diseases' effects on the agronomic performance and carotenoid content of the maize genotypes were assessed. The Genotypes differed for most agronomic traits and foliar disease resistance. Stepwise regression revealed that, although the agronomic traits determined 93% of the grain yield, each foliar disease had effect on the yield. A unit increase in maize streak virus score increased plant aspect and husk cover scores by 0.6 and 0.4, respectively, whereas an increase in common rust score decreased plant height by 16.2 cm and increased plant aspect score by 0.7. Maize streak virus and common rust decreased genotypic variability for lutein by 36.7 and 18.7%, respectively, while northern corn leaf blight decreased genotypic variability for provitamin A by 27.1%. Most of the genotypes exhibited moderate susceptibility to northern corn leaf blight. However, three selection cycles and three crosses exhibited high tolerance to maize streak virus and moderate tolerance to common rust, thus can serve as sources of PVA-enriched, maize streak virus and common rust tolerant lines. [ABSTRACT FROM AUTHOR]

Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of the residents depending on it for survival. Maize is the country’s dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.

Maize is an important crop cultivated in Sub-Saharan Africa, essential for food security. However, its cultivation faces significant challenges due to debilitating diseases such as Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV), which can lead to severe yield losses. Traditional plant disease diagnosis methods are often time-consuming and prone to errors, necessitating more efficient approaches. This study explores the application of deep learning, specifically Convolutional Neural Networks (CNNs), in the automatic detection and classification of maize diseases. We investigate six architectures: Basic CNN, EfficientNet V2 B0 and B1, LeNet-5, VGG-16, and ResNet50, using a dataset of 15344 images comprising MSV, MLN, and healthy maize leaves. Additionally, We performed hyperparameter tuning to improve the performance of the models and Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability. Our results show that the EfficientNet V2 B0 model demonstrated an accuracy of 99.99% in distinguishing between healthy and disease-infected plants. The results of this study contribute to the advancement of AI applications in agriculture, particularly in diagnosing maize diseases within Sub-Saharan Africa.

In this research, we aim to analyze a mathematical model of Maize streak virus disease as a problem of fractional optimal control. For dynamical analysis, the boundedness and uniqueness of solutions have been investi-gated and proven. Also, the basic reproduction number is obtained, and local stability conditions are given for the equilibrium points of the model. Then, an optimal control strategy is proposed for the purpose of examining the best strategy to fight the maize streak disease. We solve the fractional optimal control problem by a forward-backward sweep iterative algorithm. In this algorithm, the state variable is obtained in a forward and co-state variable by a backward method where an explicit Runge-Kutta method is used to solve differential equations arising from fractional optimal control problems. Some comparative results are presented in order to verify the model and show the efficacy of the fractional optimal control treatments.

In this research, we aim to analyze a mathematical model of Maize streak virus disease as a problem of fractional optimal control. For dynamical analysis, the boundedness and uniqueness of solutions have been investigated and proven. Also, the basic reproduction number is obtained, and local stability conditions are given for the equilibrium points of the model. Then, an optimal control strategy is proposed for the purpose of examining the best strategy to fight the maize streak disease. We solve the fractional optimal control problem by a forward-backward sweep iterative algorithm. In this algorithm, the state variable is obtained in a forward and co-state variable by a backward method where an explicit Runge-Kutta method is used to solve differential equations arising from fractional optimal control problems. Some comparative results are presented in order to verify the model and show the efficacy of the fractional optimal control treatments. [ABSTRACT FROM AUTHOR]

For pathogens infecting single host species evolutionary trade-offs have previously been demonstrated between pathogen-induced mortality rates and transmission rates. It remains unclear, however, how such trade-offs impact sub-lethal pathogen-inflicted damage, and whether these trade-offs even occur in broad host-range pathogens. Here, we examine changes over the past 110 years in symptoms induced in maize by the broad host-range pathogen, maize streak virus (MSV). Specifically, we use the quantified symptom intensities of cloned MSV isolates in differentially resistant maize genotypes to phylogenetically infer ancestral symptom intensities and check for phylogenetic signal associated with these symptom intensities. We show that whereas symptoms reflecting harm to the host have remained constant or decreased, there has been an increase in how extensively MSV colonizes the cells upon which transmission vectors feed. This demonstrates an evolutionary trade-off between amounts of pathogen-inflicted harm and how effectively viruses position themselves within plants to enable onward transmission.

In this paper, the Maize Streak Virus disease model which involves the Maize and the Homopteran population is considered. This system of the differential equation is analytically solved by using Adomian Decomposition Method. The analytical results are compared with numerical simulation by assuming certain values for the parameter. Further, the demolishing and contamination rate of contagious and receptive homopteran on receptive and contagious Maize plant (i1) i2 parameters are analyzed to reduce the contamination rate. In addition, the death rates of contagious maize, receptive homopteran and contagious homopteran (d1)d2 d3 are also discussed to remove the contagious population and make them free from Maize Streak Virus. [ABSTRACT FROM AUTHOR]

Maize is one of the most important staple food and cash crops that are largely produced by majority of smallholder farmers throughout the humid and sub-humid tropic of Africa. Despite its significance in the household food security and income, diseases, especially Maize Lethal Necrosis and Maize Streak, have been significantly affecting production of this crop. This paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania. The dataset is the largest publicly accessible dataset for maize leaves with a total of 18,148 images, which can be used to develop machine learning models for the early detection of diseases affecting maize. Moreover, the dataset can be used to support computer vision applications such as image segmentation, object detection and classification. The goal of generating this dataset is to assist the development of comprehensive tools that will help farmers in the diagnosis of diseases and the enhancement of maize yields thus eradicating the problem of fod security in Tanzania and other parts in Africa.

The maize streak virus belongs in the genus Mastrevirus, in the family Geminiviridae. The A-strain of the virus (MSV-A) is recognised as the principal causative agent of the most severe manifestation of maize streak disease (MSD). This disease continues to be a persistent limitation on maize output across sub-Saharan Africa and the nearby Indian Ocean islands. Irrespective of the causes behind the spread of MSV-A, we can determine the paths and speeds with which MSV-A spreads by analysing MSV genome sequence data along with information on when and where samples were taken. This information is valuable for identifying the geographical origins of viral strains that cause sporadic MSD epidemics in specific places and the geographical regions where viruses remain in reservoirs and contribute to prolonged epidemics during outbreaks. Our aim is to utilise these analyses to estimate the timing and origin of the MSV-A that arrived on the island of Madagascar in the Indian Ocean. Specifically, we employ model-based phylogeographic analyses on 524 complete MSV-A genome sequences, which consist of 56 newly obtained genomes from infected maize plants collected in Madagascar. These studies allow us to reconstruct the most likely paths of MSV-A to Madagascar. We found strong evidence for the existence of at least four separate movements of MSV-A variants from East and southern Africa to Madagascar. These movements took place between roughly 1979 (with a 95% highest probability density interval [HPD] ranging from 1976 to 1982) and 2003 (with a 95% HPD ranging from 2002 to 2003). While we inferred that MSV-A variants are spreading at an average rate of 38.9 km/year (with a 95% highest posterior density interval of 34.0-44.4) across their geographical range. Since their arrival in Madagascar, MSV-A variants have been migrating at an average rate of 47.6 km/year (with a 95% highest posterior density interval of 36.05-61.70). Human influences are likely significant contributors to both sporadic long-range movements of MSV-A between mainland Africa and Madagascar, as well as shorter to medium range movements within the island., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Agriculture is considered the backbone of Tanzania's economy, with more than 60% of the residents depending on it for survival. Maize is the country's dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Mayo, Maina, Mgala and Mduma.)

The recent introduction of maize chlorotic mottle virus (MCMV) and spread of the resulting maize lethal necrosis disease (MLN) in East Africa presents a threat to maize production in all Southern African countries. As MLND is the result of the synergistic action of MCMV and various cereal-infecting potyviruses, the existing virus status of maize in KwaZulu-Natal, a province of South Africa bordering Mozambique and Eswatini, was determined. Additional viruses identified in MLND affected plants in East Africa previously were also tested for in this study, including maize streak virus (MSV), Morogoro maize-associated virus (MMaV), maize-associated pteridovirus (MaPV), and two maize-associated totivirus (MATV) variants. No MCMV or potyvirus infections were detected. MSV was identified in 29% of the pools (consisting of five plants each), MaPV was detected in three plants, while only a single plant tested positive for MMaV. The presence of MMaV and MaPV was confirmed with next generation sequencing (NGS) and their complete/near complete genome sequences assembled. MMaV was also further confirmed with additional PCR primer sets. This is the first time MMaV and MaPV have been reported in South Africa. Two distinct MATV variants and two Zea mays chrysovirus 1 strains were also detected using NGS but no further tests were conducted, hence, these findings remain preliminary. Future research is required to determine the incidence and distribution of these new viruses in South Africa, their mode(s) of transmission, and effects on maize yield to determine the possible risk they may pose to the South African maize industry. [ABSTRACT FROM AUTHOR]

Maize streak disease (MSD) is the most devastating and destructive disease of maize (Zea mays L.) in sub-Saharan Africa (SSA). Field trials were conducted in the 2014 minor and 2015 major cropping seasons to screen 16 and 17 maize genotypes, respectively, for high yield and resistance to maize streak virus (MSV) infections. The plants were scored for disease severity at 4, 6, 8 and 10 weeks after planting (WAP) based on a 1-5 visual scale (1= No infection and 5= Very severe infection). Polymerase chain reaction (PCR) test was done to detect the presence of MSV in the diseased leaf samples in order to confirm field resistance. Both phenotypic and PCR test revealed that all the maize genotypes tested in the study were infected by MSV. There was a significant varietal effect on the incidence and severity of MSD in both the major and minor seasonal trials. Genotypes 'Abontem','Aburohemaa', 'Akposoe', 'Dapango', 'Dorke', 'Etubi', 'Honampa', 'Mamaba', 'Obatanpa', 'Omankwa' and PAN 12 showed mild disease symptoms during both major and minor cropping seasons. On the other hand, genotypes 'Dormabin', 'Dzinu-Eve', 'Enibi', Keta 60 and PAN 53 exhibited moderate to severe symptoms during the two cropping seasons. Incidence and severity of MSD were significantly higher in the minor season than in the major season, indicating a significant seasonal effect of MSV on the maize genotypes. The yield and yield components were observed to vary significantly among the different maize genotypes and between the cropping seasons with mean yields significantly higher in the major season than in the minor season. Genotypes 'Abontem', 'Aburohemaa', 'Akposoe', 'Dorke', 'Etubi', 'Honampa', 'Omankwa', 'Obatanpa' and PAN 12 (All improved varieties), which exhibited partial resistance to MSV infection gave high seed yields during both seasons. The improved maize genotypes that were high yielding and resistant to MSV infection should be evaluated for uniform yield trials on farmers' fields towards their release as varieties to farmers. [ABSTRACT FROM AUTHOR]

Maize streak virus (MSV) continues to be a major biotic constraint for maize production throughout Africa. Concerning the quantitative nature of inheritance of resistance to MSV disease (MSVD), we sought to identify new loci for MSV resistance in maize using F2:3 population. The mapping population was artificially inoculated with viruliferous leafhoppers under screenhouse and evaluated for MSVD resistance. Using 948 DArT markers, we identified 18 quantitative trait loci (QTLs) associated with different components of MSVD resistance accounting for 3.1–21.4% of the phenotypic variance, suggesting that a total of eleven genomic regions covering chromosomes 1, 2, 3, 4, 5 and 7 are probably required for MSVD resistance. Two new genomic regions on chromosome 4 revealed the occurrence of co-localized QTLs for different parameters associated with MSVD resistance. Moreover, the consistent appearance of QTL on chromosome 7 for MSVD resistance is illustrating the need for fine-mapping of this locus. In conclusion, these QTLs could provide additional source for breeders to develop MSV resistance. [ABSTRACT FROM AUTHOR]

In this research paper, we study the transmission dynamics of the maize streak virus disease in agriculture using fractional calculus. Our fractional-order model (FOM) consists of fractional differential equations within Caputo fractional derivative (CFD). Further, we investigate the mathematical analysis of this FOM such as the positivity and boundedness of the projected solutions, compute the control reproduction number (R c) by the next-generation matrix method, examine the local stability by using Routh–Hurwitz conditions and global stability by using fractional LaSalle's invariance principle for all possible equilibrium points. A sensitivity analysis is conducted on the parameters integrated into R c to evaluate how accurately the parameter estimation fits. Additionally, we present further numerical results on the FOM to demonstrate the obtained analytical results. Then, we formulate the fractional optimal control problem (FOCP) depending on the suggested FOM with three control efforts u 1 , u 2 and u 3 called respectively, prevention, quarantine and insecticide chemical. Also, we study this FOCP analytically to derive the fractional necessary optimality conditions (NOCs) based on a kind of Pontryagin's maximum principle. After that, we solve these fractional NOCs numerically where the state and co-state variables are subject to the left CFD. We present various strategies with different combinations of suggested controls to examine the best strategy for decreasing the transmission of MSV in maize plants, where each one of these strategies is able to reduce the maize streak disease (MSD) at the period of control time. We perform simulations of outcomes using MATLAB by developing the forward–backward sweep method utilizing the fractional Euler method. Moreover, some figures are presented to show the impact of both control interventions, change in weight factors and fractional order within the suggested strategies. We can observe that the fractional order plays a significant role in limiting the spread of MSD where the obtained results illustrated that the MSD can be reduced through the suggested FOM, which has a notable influence on plant epidemiology. [ABSTRACT FROM AUTHOR]

Maize streak virus (MSV) is responsible for streak disease of maize and poses a serious threat to maize production in sub-Saharan Africa. Polygenic resistance to MSV has become an essential requirement in modern maize cultivars to mitigate yield losses. Many single nucleotide polymorphism (SNP) markers linked to putative MSV resistance loci have been identified for use in forward breeding. This study aimed to validate, using the high-throughput kompetitive allele specific PCR (KASP) assay, the diagnostic ability of the three SNP markers linked to the loci for the Msv1 resistance trait in 151 early generations inbred lines with diverse genetic backgrounds, together with nine MSV-resistant elite lines and a susceptible check (cv. Pool-16). The phenotypic responses were determined by MSV inoculation using viruliferous leafhoppers (Cicadulina triangular) under screenhouse conditions. Based on an established MSV disease rating system, the maize lines were categorized into resistant, moderately resistant, susceptible, and highly susceptible. The three SNPs associated with MSV resistance were detected in 133 lines, which were categorized as resistant (54), moderately resistant (76), and susceptible (1). The 18 early generation lines without these SNPs were classified as moderately resistant (10), susceptible (5), and highly susceptible (3). This study confirms the strong association of SNPs with MSV resistance and their usefulness for forward breeding in maize while emphasizing the need to identify additional markers to screen lines for MSV resistance without any ambiguity.

Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVIrededge), Simple Ratio (SR), green Chlorophyll Index (CIgreen), and Rededge Chlorophyll Index (CIrededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CIgreen, CIrededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates.

Os objectivos deste trabalho consistiram em avaliar o comportamento de cultivares regionais de milho - ‘Branco redondo’ e ‘SAM3’ - e importadas - ‘ZM423’, ‘ZM521’, ‘ZM523’, ‘ZM611’, ‘ZM621’ - quanto à sua sensibilidade a brocas do colmo e da espiga e à virose MSV (Maize Streak Virus). Os ensaios realizaram-se em três locais do Planalto Central de Angola na época seca e na das chuvas. Nas duas épocas e nos três locais, não se registaram infecções pelo vírus MSV nas cultivares regionais; todavia, nas importadas houve incidência da doença embora com baixa severidade. Em relação às brocas, não se observaram diferenças significativas entre as cultivares e a severidade foi baixa. A produção das cultivares importadas foi superior na época seca, com um rendimento entre as 7 e 8 t ha-1. Em sequeiro (época das chuvas) as cultivares importadas produziram, em média, entre 3 a 4 t ha-1. As cultivares locais não mostraram uma variação tão acentuada no rendimento entre as duas épocas. Em geral, os valores médios do rendimento da ’Branco redondo’ e ‘SAM3’ foram de 2 t ha-1, com excepção desta última na Chianga, que atingiu as 4 t ha-1 em sequeiro. A supremacia da ‘SAM3’ na época chuvosa na Chianga deve-se certamente ao facto de ter sido aí seleccionada.The main objectives of this work were the evaluation of the behaviour of different traditional maize varieties - ‘Branco redondo’ and ‘SAM3’ - and imported ones - ‘ZM423’, ‘ZM521’, ‘ZM523’, ‘ZM611’, ‘ZM621’ - to their tolerance to the stem and earn borars and to the virus MSV (Maize Streak Virus), in three locations of the central highlands of Angola and in two cropping seasons (rainy and dry season). Maize yields were also evaluated. In the two growing seasons and in the three locations, the traditional varieties did not show any infection by the virus MSV. The imported varieties show disease symptoms but at a low incidence level. The severity of the maize stem and earn borars were low and no significant differences were observed between the tested varieties. The yields of the imported varieties varied between 7 and 8 t ha-1 in the dry season (irrigation) but in the rainy season their average yield decreased (3 to 4 t ha-1). The yield of the traditional varieties was similar in both growing seasons, in general lower than the imported ones. The average yield in ’Branco redondo’ and ‘SAM3’ varieties was 2 t ha-1. ‘SAM3’ variety production in Chianga was an exception; where the yield was 4 t ha-1 in the rainy season. Probably this can be explained by the fact that ‘SAM3’ was obtained in this region.