Microcomputer-Based Quantification of Maize Streak Virus Symptoms in Zea mays

Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

The severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement-often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.

Maize streak virus research in Africa: an end or a crossroad

The economic importance of the maize streak virus disease to the African maize production dynamic is to be appreciated now more than ever due to the preponderant influence of a changing climate. Continued dependence on a single major-effect quantitative trait locus (QTL) called Msv1 on Chromosome 1 of Maize (Zea mays L.) is not guaranteed to ensure durable resistance to the causal pathogen. With over ten decades of research on the disease and its associated host plant resistance mechanisms, it is pertinent to consider future approaches to attaining durability by looking to the synergistic roles of moderate- and minor-effect QTLs located on other chromosomes so as to facilitate a secure farming system for sub-Saharan Africa. For this review, more than 40 publications relating to maize streak disease research were methodically analysed with about 30% making specific reference to conventional, molecular and transgenic approaches employed in introgressing, maintaining and improving streak resistance in maize. A meta-analysis of mapped QTLs conferring streak resistance was conducted in a bid to reveal any inter-dependence or co-localization of resistant loci and to aid decision-making for marker-assisted breeding. With the changing climatic conditions around the globe, man's preparedness in the event of an epidemic following any evolutionary process in the streak viral genome was determined as insufficient. Modern breeding approaches including gene pyramiding that could be considered in maize breeding programmes to ensure durability for streak resistance were proposed while improving maize for other abiotic stress tolerance, particularly drought.

Abdul Majeed A. A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

Standard Area Diagrams for Aiding Severity Estimation: Scientometrics, Pathosystems, and Methodological Trends in the Last 25 Years

Standard area diagrams (SAD) have long been used as a tool to aid the estimation of plant disease severity, an essential variable in phytopathometry. Formal validation of SAD was not considered prior to the early 1990s, when considerable effort began to be invested developing SAD and assessing their value for improving accuracy of estimates of disease severity in many pathosystems. Peer-reviewed literature post-1990 was identified, selected, and cataloged in bibliographic software for further scrutiny and extraction of scientometric, pathosystem-related, and methodological-related data. In total, 105 studies (127 SAD) were found and authored by 327 researchers from 10 countries, mainly from Brazil. The six most prolific authors published at least seven studies. The scientific impact of a SAD article, based on annual citations after publication year, was affected by disease significance, the journal's impact factor, and methodological innovation. The reviewed SAD encompassed 48 crops and 103 unique diseases across a range of plant organs. Severity was quantified largely by image analysis software such as QUANT, APS-Assess, or a LI-COR leaf area meter. The most typical SAD comprised five to eight black-and-white drawings of leaf diagrams, with severity increasing nonlinearly. However, there was a trend toward using true-color photographs or stylized representations in a range of color combinations and more linear (equally spaced) increments of severity. A two-step SAD validation approach was used in 78 of 105 studies for which linear regression was the preferred method but a trend toward using Lin's correlation concordance analysis and hypothesis tests to detect the effect of SAD on accuracy was apparent. Reliability measures, when obtained, mainly considered variation among rather than within raters. The implications of the findings and knowledge gaps are discussed. A list of best practices for designing and implementing SAD and a website called SADBank for hosting SAD research data are proposed.

Plant Disease Severity Assessment-How Rater Bias, Assessment Method, and Experimental Design Affect Hypothesis Testing and Resource Use Efficiency

The effect of rater bias and assessment method on hypothesis testing was studied for representative experimental designs for plant disease assessment using balanced and unbalanced data sets. Data sets with the same number of replicate estimates for each of two treatments are termed "balanced" and those with unequal numbers of replicate estimates are termed "unbalanced". The three assessment methods considered were nearest percent estimates (NPEs), an amended 10% incremental scale, and the Horsfall-Barratt (H-B) scale. Estimates of severity of Septoria leaf blotch on leaves of winter wheat were used to develop distributions for a simulation model. The experimental designs are presented here in the context of simulation experiments which consider the optimal design for the number of specimens (individual units sampled) and the number of replicate estimates per specimen for a fixed total number of observations (total sample size for the treatments being compared). The criterion used to gauge each method was the power of the hypothesis test. As expected, at a given fixed number of observations, the balanced experimental designs invariably resulted in a higher power compared with the unbalanced designs at different disease severity means, mean differences, and variances. Based on these results, with unbiased estimates using NPE, the recommended number of replicate estimates taken per specimen is 2 (from a sample of specimens of at least 30), because this conserves resources. Furthermore, for biased estimates, an apparent difference in the power of the hypothesis test was observed between assessment methods and between experimental designs. Results indicated that, regardless of experimental design or rater bias, an amended 10% incremental scale has slightly less power compared with NPEs, and that the H-B scale is more likely than the others to cause a type II error. These results suggest that choice of assessment method, optimizing sample number and number of replicate estimates, and using a balanced experimental design are important criteria to consider to maximize the power of hypothesis tests for comparing treatments using disease severity estimates.

An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

BACKGROUND

Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance.

RESULTS

In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study.

CONCLUSIONS

Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.

Disease Severity Estimates-Effects of Rater Accuracy and Assessment Methods for Comparing Treatments

Assessment of disease severity is required for several purposes in plant pathology; most often, the estimates are made visually. It is established that visual estimates can be inaccurate and unreliable. The ramifications of biased or imprecise estimates by raters have not been fully explored using empirical data, partly because of the logistical difficulties involved in different raters assessing the same leaves for which actual disease has been measured in a replicated experiment with multiple treatments. In this study, nearest percent estimates (NPEs) of Septoria leaf blotch (SLB) on leaves of winter wheat from nontreated and fungicide-treated plots were assessed in both 2006 and 2007 by four raters and compared with assumed actual values measured using image analysis. Lin's concordance correlation (LCC, ρ_c) was used to assess agreement between the two approaches. NPEs were converted to Horsfall-Barratt (HB) midpoints and were compared with actual values. The estimates of SLB severity from fungicide-treated and nontreated plots were analyzed using generalized linear mixed modeling to ascertain effects of rater using both the NPE and HB values. Rater 1 showed good accuracy (ρ_c = 0.986 to 0.999), while raters 3 and 4 were less accurate (ρ_c = 0.205 to 0.936). Conversion to the HB scale had little effect on bias but reduced numerically both precision and accuracy for most raters on most assessment dates (precision, r = -0.001 to -0.132; and accuracy, ρ_c = -0.003 to -0.468). Interrater reliability was also reduced slightly by conversion of estimates to HB midpoint values. Estimates of mean SLB severity were significantly different between image analysis and raters 2, 3, and 4, and there were frequently significant differences among raters (F = 151 to 1,260, P = 0.001 to P < 0.0001). Only on 26 June 2007 did conversion to the HB scale change the means separation ranking of rater estimates. Nonetheless, image analysis and all raters were able to differentiate control and treated-plot treatments (F = 116 to 1,952, P = 0.002 to P < 0.0001, depending on date and rater). Conversion of NPEs to the HB scale tended to reduce F values slightly (2006: NPEs, F = 116 to 276, P = 0.002 to 0.0005; and, for the HB-converted values, F = 101 to 270, P = 0.002 to 0.0005; 2007: NPEs, F = 164 to 1,952, P = 0.001 to P < 0.0001; and, for HB-converted values, F = 126 to 1,633, P = 0.002 to P < 0.0001). The results reaffirm the need for accurate and reliable disease assessment to minimize over- or underestimates compared with actual disease, and the data we present support the view that, where multiple raters are deployed, they should be assigned in a manner to reduce any potential effect of rater differences on the analysis.

An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image Processing

A method is presented to detect and quantify leaf symptoms using conventional color digital images. The method was designed to be completely automatic, eliminating the possibility of human error and reducing time taken to measure disease severity. The program is capable of dealing with images containing multiple leaves, further reducing the time taken. Accurate results are possible when the symptoms and leaf veins have similar color and shade characteristics. The algorithm is subject to one constraint: the background must be as close to white or black as possible. Tests showed that the method provided accurate estimates over a wide variety of conditions, being robust to variation in size, shape, and color of leaves; symptoms; and leaf veins. Low rates of false positives and false negatives occurred due to extrinsic factors such as issues with image capture and the use of extreme file compression ratios.

Measuring quantitative virulence in the wheat pathogen Zymoseptoria tritici using high-throughput automated image analysis

Zymoseptoria tritici, causal agent of Septoria tritici blotch on wheat, produces pycnidia in chlorotic and necrotic lesions on infected leaves. A high-throughput phenotyping method was developed based on automated digital image analysis that accurately measures the percentage of leaf area covered by lesions (PLACL) as well as pycnidia size and number. A seedling inoculation assay was conducted using 361 Z. tritici isolates originating from a controlled cross and two different winter wheat cultivars. Pycnidia size and density were found to be quantitative traits that showed a continuous distribution in the progeny. There was a weak correlation between pycnidia density and size (r = -0.27) and between pycnidia density and PLACL (r = 0.37). There were significant differences in PLACL and pycnidia density on resistant and susceptible cultivars. In all, >20% of the offspring exhibited significantly different pycnidia density on the two cultivars, consistent with host specialization. Automated image analysis provided greater accuracy and precision compared with traditional visual estimates of virulence. These results show that digital image analysis provides a powerful tool for measuring differences in quantitative virulence among strains of Z. tritici.

Extensive recombination-induced disruption of genetic interactions is highly deleterious but can be partially reversed by small numbers of secondary recombination events

Although homologous recombination can potentially provide viruses with vastly more evolutionary options than are available through mutation alone, there are considerable limits on the adaptive potential of this important evolutionary process. Primary among these is the disruption of favorable coevolved genetic interactions that can occur following the transfer of foreign genetic material into a genome. Although the fitness costs of such disruptions can be severe, in some cases they can be rapidly recouped by either compensatory mutations or secondary recombination events. Here, we used a maize streak virus (MSV) experimental model to explore both the extremes of recombination-induced genetic disruption and the capacity of secondary recombination to adaptively reverse almost lethal recombination events. Starting with two naturally occurring parental viruses, we synthesized two of the most extreme conceivable MSV chimeras, each effectively carrying 182 recombination breakpoints and containing thorough reciprocal mixtures of parental polymorphisms. Although both chimeras were severely defective and apparently noninfectious, neither had individual movement-, encapsidation-, or replication-associated genome regions that were on their own "lethally recombinant." Surprisingly, mixed inoculations of the chimeras yielded symptomatic infections with viruses with secondary recombination events. These recombinants had only 2 to 6 breakpoints, had predominantly inherited the least defective of the chimeric parental genome fragments, and were obviously far more fit than their synthetic parents. It is clearly evident, therefore, that even when recombinationally disrupted virus genomes have extremely low fitness and there are no easily accessible routes to full recovery, small numbers of secondary recombination events can still yield tremendous fitness gains. Importance: Recombination between viruses can generate strains with enhanced pathological properties but also runs the risk of producing hybrid genomes with decreased fitness due to the disruption of favorable genetic interactions. Using two synthetic maize streak virus genome chimeras containing alternating genome segments derived from two natural viral strains, we examined both the fitness costs of extreme degrees of recombination (both chimeras had 182 recombination breakpoints) and the capacity of secondary recombination events to recoup these costs. After the severely defective chimeras were introduced together into a suitable host, viruses with between 1 and 3 secondary recombination events arose, which had greatly increased replication and infective capacities. This indicates that even in extreme cases where recombination-induced genetic disruptions are almost lethal, and 91 consecutive secondary recombination events would be required to reconstitute either one of the parental viruses, moderate degrees of fitness recovery can be achieved through relatively small numbers of secondary recombination events.

Digital image processing techniques for detecting, quantifying and classifying plant diseases

ABSTRACT

This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.

Application of image analysis in studies of quantitative disease resistance, exemplified using common bacterial blight-common bean pathosystem

The effectiveness of image analysis (IA) compared with an ordinal visual scale, for quantitative measurement of disease severity, its application in quantitative genetic studies, and its effect on the estimates of genetic parameters were investigated. Studies were performed using eight backcross-derived families of common bean (Phaseolus vulgaris) (n = 172) segregating for the molecular marker SU91, known to be associated with a quantitative trait locus (QTL) for resistance to common bacterial blight (CBB), caused by Xanthomonas campestris pv. phaseoli and X. fuscans subsp. fuscans. Even though both IA and visual assessments were highly repeatable, IA was more sensitive in detecting quantitative differences between bean genotypes. The CBB phenotypic difference between the two SU91 genotypic groups was consistently more than fivefold for IA assessments but generally only two- to threefold for visual assessments. Results suggest that the visual assessment results in overestimation of the effect of QTL in genetic studies. This may have been caused by lack of additivity and uneven intervals of the visual scale. Although visual assessment of disease severity is a useful tool for general selection in breeding programs, assessments using IA may be more suitable for phenotypic evaluations in quantitative genetic studies involving CBB resistance as well as other foliar diseases.

Smart sensor for real-time quantification of common symptoms present in unhealthy plants

Plant responses to physiological function disorders are called symptoms and they are caused principally by pathogens and nutritional deficiencies. Plant symptoms are commonly used as indicators of the health and nutrition status of plants. Nowadays, the most popular method to quantify plant symptoms is based on visual estimations, consisting on evaluations that raters give based on their observation of plant symptoms; however, this method is inaccurate and imprecise because of its obvious subjectivity. Computational Vision has been employed in plant symptom quantification because of its accuracy and precision. Nevertheless, the systems developed so far lack in-situ, real-time and multi-symptom analysis. There exist methods to obtain information about the health and nutritional status of plants based on reflectance and chlorophyll fluorescence, but they use expensive equipment and are frequently destructive. Therefore, systems able of quantifying plant symptoms overcoming the aforementioned disadvantages that can serve as indicators of health and nutrition in plants are desirable. This paper reports an FPGA-based smart sensor able to perform non-destructive, real-time and in-situ analysis of leaf images to quantify multiple symptoms presented by diseased and malnourished plants; this system can serve as indicator of the health and nutrition in plants. The effectiveness of the proposed smart-sensor was successfully tested by analyzing diseased and malnourished plants.

Recombination hotspots and host susceptibility modulate the adaptive value of recombination during maize streak virus evolution

BACKGROUND

Maize streak virus -strain A (MSV-A; Genus Mastrevirus, Family Geminiviridae), the maize-adapted strain of MSV that causes maize streak disease throughout sub-Saharan Africa, probably arose between 100 and 200 years ago via homologous recombination between two MSV strains adapted to wild grasses. MSV recombination experiments and analyses of natural MSV recombination patterns have revealed that this recombination event entailed the exchange of the movement protein - coat protein gene cassette, bounded by the two genomic regions most prone to recombination in mastrevirus genomes; the first surrounding the virion-strand origin of replication, and the second around the interface between the coat protein gene and the short intergenic region. Therefore, aside from the likely adaptive advantages presented by a modular exchange of this cassette, these specific breakpoints may have been largely predetermined by the underlying mechanisms of mastrevirus recombination. To investigate this hypothesis, we constructed artificial, low-fitness, reciprocal chimaeric MSV genomes using alternating genomic segments from two MSV strains; a grass-adapted MSV-B, and a maize-adapted MSV-A. Between them, each pair of reciprocal chimaeric genomes represented all of the genetic material required to reconstruct - via recombination - the highly maize-adapted MSV-A genotype, MSV-MatA. We then co-infected a selection of differentially MSV-resistant maize genotypes with pairs of reciprocal chimaeras to determine the efficiency with which recombination would give rise to high-fitness progeny genomes resembling MSV-MatA.

RESULTS

Recombinants resembling MSV-MatA invariably arose in all of our experiments. However, the accuracy and efficiency with which the MSV-MatA genotype was recovered across all replicates of each experiment depended on the MSV susceptibility of the maize genotypes used and the precise positions - in relation to known recombination hotspots - of the breakpoints required to re-create MSV-MatA. Although the MSV-sensitive maize genotype gave rise to the greatest variety of recombinants, the measured fitness of each of these recombinants correlated with their similarity to MSV-MatA.

CONCLUSIONS

The mechanistic predispositions of different MSV genomic regions to recombination can strongly influence the accessibility of high-fitness MSV recombinants. The frequency with which the fittest recombinant MSV genomes arise also correlates directly with the escalating selection pressures imposed by increasingly MSV-resistant maize hosts.

Automated Image Analysis of the Severity of Foliar Citrus Canker Symptoms

Citrus canker (caused by Xanthomonas citri subsp. citri) is a destructive disease, reducing yield and rendering fruit unfit for fresh sale. Accurate assessment of citrus canker severity and other diseases is needed for several purposes, including monitoring epidemics and evaluation of germplasm. We compared measurements of citrus canker severity (percent area infected) from automated image analysis to visual estimates by raters and true values using images from five leaf samples (65, 123, 50, 50, and 200 leaves; disease severity from 0 to 60%). Severity on leaves was measured by automated image analysis by (i) basing threshold values on a presample of leaves, or (ii) replacing healthy leaf color on a leaf-by-leaf basis before automating image analysis. Samples 1 to 4 were assessed by three trained plant pathologists, and sample 5 was assessed by an additional 25 raters. Healthy leaf area color replacement gave the most consistent agreement with the true severity data. Using color replacement, agreement with true values based on Lin's concordance correlation coefficient (ρ_c) was 0.93, 0.79, 0.71, 0.85, and 0.89 for each of the samples, respectively. The range and consistency of agreement was generally less good for automated thresholds based on a presample (ρ_c = 0.35-0.90) or visual raters (ρ_c = 0.30-0.94). The constituents of agreement (precision and accuracy) showed similar trends. No one rater or method was best for every leaf sample, but replacing healthy color in each leaf with a standard color before automation of image analysis improved agreement, and was relatively quick (20 s per image). The accuracy and precision of automated image analysis of citrus canker severity can be comparable to unaided, direct visual estimation by many raters.

Comparison of Assessment of Citrus Canker Foliar Symptoms by Experienced and Inexperienced Raters

Citrus canker (Xanthomonas citri subsp. citri) is destructive in many citrus production regions in tropical and subtropical parts of the world. Assessment of canker symptoms is required for diverse reasons, including monitoring epidemics, evaluating the efficacy of control strategies, and disease response in breeding material. The objectives were to compare the ability of experienced and inexperienced raters at assessing citrus canker, to identify factors that affect the quality of the assessment, to determine common sources of error, and to discern how error is related to actual disease magnitude. Two-hundred digital leaf images (0 to 37% area infected) were assessed once by 28 raters, five of whom were experienced plant pathologists (PPs), and 23 who had no experience in disease severity assessment (NPPs). True disease (lesion number [LN], % necrotic area [%N], and % chlorotic+necrotic area [%CN]) was measured using image analysis on a leaf-by-leaf basis, and each parameter was estimated by the 28 raters. LN was neither severely over- nor underestimated, while %N was greatly overestimated, with a lesser tendency to overestimate %CN over the true severity range of these two symptom types. A linear relationship existed between estimate of the disease and true disease for all measures of severity. Data were heteroscedastic and error was not constant with increasing true disease. Agreement between rater estimates and true disease was measured with Lin's concordance correlation coefficient (ρ_c). LN showed greatest agreement (ρ_c = 0.88 to 0.99), followed by %CN (ρ_c = 0.80 to 0.95) and %N (ρ_c = 0.19 to 0.84). Greater lesion number resulted in overestimation of area infected for both %N and %CN. Overestimation was particularly noticeable at low disease severities. There was a linear relationship between log variance and log true disease for LN (r² = 0.71), %N (r² = 0.85), and %CN (r² = 0.88), and raters tended to estimate disease above 10% to the nearest 5 or 10%. GLM analysis showed differences between PP and NPP groups in assessing disease. For LN, precision of assessment for both groups was similar (r² > 0.92 and 0.94, respectively), but for estimates of %N and %CN, the PPs were more precise (%N and %CN, r² = 0.61 and 0.73, respectively) compared to NPPs (%N and %CN, r² = 0.45 and 0.58, respectively). Absolute error for mean LN was low. The absolute error of %N and %CN showed overestimation to approximately 8% area infected. Above 8%, absolute error increased, but comprised both over- and underestimation. For %N and %CN, relative error was almost exclusively positive and dramatic at severity <8% (up to approximately 600%), but at severity >10% it was relatively small. Error in rater estimates of canker severity is ubiquitous. Understanding these sources of error will aid in the development of both appropriate training and relevant rating aids.

Rapid host adaptation by extensive recombination

Experimental investigations into virus recombination can provide valuable insights into the biochemical mechanisms and the evolutionary value of this fundamental biological process. Here, we describe an experimental scheme for studying recombination that should be applicable to any recombinogenic viruses amenable to the production of synthetic infectious genomes. Our approach is based on differences in fitness that generally exist between synthetic chimaeric genomes and the wild-type viruses from which they are constructed. In mixed infections of defective reciprocal chimaeras, selection strongly favours recombinant progeny genomes that recover a portion of wild-type fitness. Characterizing these evolved progeny viruses can highlight both important genetic fitness determinants and the contribution that recombination makes to the evolution of their natural relatives. Moreover, these experiments supply precise information about the frequency and distribution of recombination breakpoints, which can shed light on the mechanistic processes underlying recombination. We demonstrate the value of this approach using the small single-stranded DNA geminivirus, maize streak virus (MSV). Our results show that adaptive recombination in this virus is extremely efficient and can yield complex progeny genomes comprising up to 18 recombination breakpoints. The patterns of recombination that we observe strongly imply that the mechanistic processes underlying rolling circle replication are the prime determinants of recombination breakpoint distributions found in MSV genomes sampled from nature.

Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software

A digital image analysis method previously used to evaluate leaf color changes due to nutritional changes was modified to measure the severity of several foliar fungal diseases. Images captured with a flatbed scanner or digital camera were analyzed with a freely available software package, Scion Image, to measure changes in leaf color caused by fungal sporulation or tissue damage. High correlations were observed between the percent diseased leaf area estimated by Scion Image analysis and the percent diseased leaf area from leaf drawings. These drawings of various foliar diseases came from a disease key previously developed to aid in visual estimation of disease severity. For leaves of Nicotiana benthamiana inoculated with different spore concentrations of the anthracnose fungus Colletotrichum destructivum, a high correlation was found between the percent diseased tissue measured by Scion Image analysis and the number of leaf spots. The method was adapted to quantify percent diseased leaf area ranging from 0 to 90% for anthracnose of lily-of-the-valley, apple scab, powdery mildew of phlox and rust of golden rod. In some cases, the brightness and contrast of the images were adjusted and other modifications were made, but these were standardized for each disease. Detached leaves were used with the flatbed scanner, but a method using attached leaves with a digital camera was also developed to make serial measurements of individual leaves to quantify symptom progression. This was successfully applied to monitor anthracnose on N. benthamiana leaves. Digital image analysis using Scion Image software is a useful tool for quantifying a wide variety of fungal interactions with plant leaves.

Visual Rating and the Use of Image Analysis for Assessing Different Symptoms of Citrus Canker on Grapefruit Leaves

Citrus canker is caused by the bacterial pathogen Xanthomonas axonopodis pv. citri and infects several citrus species in wet tropical and subtropical citrus growing regions. Accurate, precise, and reproducible disease assessment is needed for monitoring epidemics and disease response in breeding material. The objective of this study was to assess reproducibility of image analysis (IA) for measuring severity of canker symptoms and to compare this to visual assessments made by three visual raters (VR1-3) for various symptom types (lesion numbers, % area necrotic, and % area necrotic+chlorotic), and to assess inter- and intra-VR reproducibility. Digital images of 210 citrus leaves with a range of symptom severity were assessed on two separate occasions. IA was more precise than VRs for all symptom types (inter-assessment correlation coefficients, r, for lesion numbers by IA = 0.99, by VRs = 0.89 to 0.94; for %, r for % area necrotic+chlorotic for IA = 0.97 and for VRs = 0.86 to 0.89; and r for % area necrotic for IA = 0.96 and for VRs = 0.74 to 0.85). Accuracy based on Lin's concordance coefficient also followed a similar pattern, with IA being most consistently accurate for all symptom types (bias correction factor, C_b = 0.99 to 1.00) compared to visual raters (C_b = 0.85 to 1.00). Lesion number was the most reproducible symptom assessment (Lin's concordance correlation coefficient, ρ_c, = 0.76 to 0.99), followed by % area necrotic+chlorotic (ρ_c = 0.85 to 0.97), and finally % area necrotic (ρ_c = 0.72 to 0.96). Based on the "true" value provided by IA, precision among VRs was reasonable for number of lesions per leaf (r = 0.88 to 0.94), slightly less precision for % area necrotic+chlorotic (r = 0.87 to 0.92), and poorest precision for % area necrotic (r = 0.77 to 0.83). Loss in accuracy was less, but showed a similar trend with counts of lesion numbers (C_b = 0.93 to 0.99) which was more consistently accurately reproduced by VRs than either % area necrotic (C_b = 0.85 to 0.99) or % area necrotic+chlorotic (C_b = 0.91 to 1.00). Thus, visual raters suffered losses in both precision and accuracy, with loss in precision estimating % area necrotic being the greatest. Indeed, only for % area necrotic was there a significant effect of rater (a two-way random effects model ANOVA returned a P < 0.001 and 0.016 for rater in assessments 1 and 2, respectively). VRs showed a marked preference for clustering of % area severity estimates, especially at severity >20% area (e.g., 25, 30, 35, 40, etc.), yet VRs were prepared to estimate disease of <1% area, and at 1% increments up to 20%. There was a linear relationship between actual disease (IA assessments) and VRs. IA appears to provide a highly reproducible way to assess canker-infected leaves for disease, but symptom characters (symptom heterogeneity, coalescence of lesions) could lead to discrepancies in results, and full automation of the system remains to be tested.

Maize streak virus-resistant transgenic maize: a first for Africa

In this article, we report transgene-derived resistance in maize to the severe pathogen maize streak virus (MSV). The mutated MSV replication-associated protein gene that was used to transform maize showed stable expression to the fourth generation. Transgenic T2 and T3 plants displayed a significant delay in symptom development, a decrease in symptom severity and higher survival rates than non-transgenic plants after MSV challenge, as did a transgenic hybrid made by crossing T2 Hi-II with the widely grown, commercial, highly MSV-susceptible, white maize genotype WM3. To the best of our knowledge, this is the first maize to be developed with transgenic MSV resistance and the first all-African-produced genetically modified crop plant.

Inhibition of maize streak virus (MSV) replication by transient and transgenic expression of MSV replication-associated protein mutants

Maize streak disease is a severe agricultural problem in Africa and the development of maize genotypes resistant to the causal agent, Maize streak virus (MSV), is a priority. A transgenic approach to engineering MSV-resistant maize was developed and tested in this study. A pathogen-derived resistance strategy was adopted by using targeted deletions and nucleotide-substitution mutants of the multifunctional MSV replication-associated protein gene (rep). Various rep gene constructs were tested for their efficacy in limiting replication of wild-type MSV by co-bombardment of maize suspension cells together with an infectious genomic clone of MSV and assaying replicative forms of DNA by quantitative PCR. Digitaria sanguinalis, an MSV-sensitive grass species used as a model monocot, was then transformed with constructs that had inhibited virus replication in the transient-expression system. Challenge experiments using leafhopper-transmitted MSV indicated significant MSV resistance--from highly resistant to immune--in regenerated transgenic D. sanguinalis lines. Whereas regenerated lines containing a mutated full-length rep gene displayed developmental and growth defects, those containing a truncated rep gene both were fertile and displayed no growth defects, making the truncated gene a suitable candidate for the development of transgenic MSV-resistant maize.

Restoration of native folding of single-stranded DNA sequences through reverse mutations: an indication of a new epigenetic mechanism

We used in vivo (biological), in silico (computational structure prediction), and in vitro (model sequence folding) analyses of single-stranded DNA sequences to show that nucleic acid folding conservation is the selective principle behind a high-frequency single-nucleotide reversion observed in a three-nucleotide mutated motif of the Maize streak virus replication associated protein (Rep) gene. In silico and in vitro studies showed that the three-nucleotide mutation adversely affected Rep nucleic acid folding, and that the single-nucleotide reversion [C(601)A] restored wild-type-like folding. In vivo support came from infecting maize with mutant viruses: those with Rep genes containing nucleotide changes predicted to restore a wild-type-like fold [A(601)/G(601)] preferentially accumulated over those predicted to fold differently [C(601)/T(601)], which frequently reverted to A(601) and displaced the original population. We propose that the selection of native nucleic acid folding is an epigenetic effect, which might have broad implications in the evolution of plants and their viruses.

A three-nucleotide mutation altering the Maize streak virus Rep pRBR-interaction motif reduces symptom severity in maize and partially reverts at high frequency without restoring pRBR–Rep binding

Geminivirus infectivity is thought to depend on interactions between the virus replication-associated proteins Rep or RepA and host retinoblastoma-related proteins (pRBR), which control cell-cycle progression. It was determined that the substitution of two amino acids in the Maize streak virus (MSV) RepA pRBR-interaction motif (LLCNE to LLCLK) abolished detectable RepA–pRBR interaction in yeast without abolishing infectivity in maize. Although the mutant virus was infectious in maize, it induced less severe symptoms than the wild-type virus. Sequence analysis of progeny viral DNA isolated from infected maize enabled detection of a high-frequency single-nucleotide reversion of C(601)A in the 3 nt mutated sequence of the Rep gene. Although it did not restore RepA–pRBR interaction in yeast, sequence-specific PCR showed that, in five out of eight plants, the C(601)A reversion appeared by day 10 post-inoculation. In all plants, the C(601)A revertant eventually completely replaced the original mutant population, indicating a high selection pressure for the single-nucleotide reversion. Apart from potentially revealing an alternative or possibly additional function for the stretch of DNA that encodes the apparently non-essential pRBR-interaction motif of MSV Rep, the consistent emergence and eventual dominance of the C(601)A revertant population might provide a useful tool for investigating aspects of MSV biology, such as replication, mutation and evolution rates, and complex population phenomena, such as competition between quasispecies and population turnover.

Investigation of Maize streak virus pathogenicity determinants using chimaeric genomes

Genes and intergenic regions were reciprocally exchanged between a highly pathogenic Maize streak virus (MSV) isolate (MSV-MatA) and three less pathogenic isolates (MSV-Kom, MSV-R2, and MSV-VW) to determine the contribution of individual genome constituents to MSV pathogenicity in maize. Comparison of disease symptoms produced by the 54 resulting chimaeras and parental viruses enabled identification of genome constituents that are primarily responsible for the heightened pathogenicity of MSV-MatA in maize. Whereas pathogenicity determinants were detected in all of the MSV genomic regions examined, generally only chimaeras containing the MSV-MatA long intergenic region, coat protein gene, and/or movement protein gene were more pathogenic than the milder MSV isolates from which most of their genomes were derived. The pathogenicity of chimeras was strongly influenced by the relatedness of their parental viruses and evidence was found of nucleotide sequence-dependent interactions between both coding and intergenic regions.

Co-adaptation between cassava mosaic geminiviruses and their local vector populations

Four cassava mosaic geminivirus (CMG) isolates; African cassava mosaic virus from Namulonge, Uganda (ACMV-[Nam]), East African cassava mosaic virus from Mtwara, Tanzania (EACMV-[Mtw]), EACMV-Uganda from Namulonge (EACMV-UG[Nam]) and Indian cassava mosaic virus from Trivandrum, India (ICMV-[Tri]) were compared for their ability to be transmitted by four colonies of cassava whitefly, Bemisia tabaci (Gennadius), collected from Namulonge (NAM), Mtwara (MTW), Kumasi (KUM) and Trivandrum (TRI). With 20 adult whiteflies per test plant, the CMGs from Africa were transmitted by all three of the African B. tabaci populations to 60-79% of the target plants. Indian cassava B. tabaci transmitted ICMV-[Tri] to 89% of the target plants. In contrast, Indian cassava B. tabaci transmitted EACMV-[Mtw] and Tanzanian cassava B. tabaci transmitted ICMV-[Tri] less than a tenth as often, even when using 50 adults per plant and with increased acquisition and inoculation access periods. The complete coat protein genes of the CMGs had sequences typical of their source viruses, the major differences occurring between those originating from India and Africa. Symptom severity of the CMGs was quantified precisely by both visual assessment and image analysis techniques. EACMV-[Mtw] and ACMV-[Nam] were the most and least damaging isolates with 15.4 and 10.0% of the leaf area diseased, respectively. In these tests the transmission frequency was not linked to symptom severity in the source plants. These data support the hypothesis that virus-vector co-adaptation exists in the cassava mosaic disease (CMD) pathosystem and the results are discussed in relation to CMD epidemiology.

Biological and Genomic Sequence Characterization of Maize streak virus Isolates from Wheat

ABSTRACT Maize streak virus (MSV) is best known as the causal agent of maize streak disease. However, only a genetically uniform subset of the viruses within this diverse species is actually capable of producing severe symptoms in maize. Whereas these "maize-type" viruses all share greater than 95% sequence identity, MSV strains isolated from grasses may share as little as 79% sequence identity with the maize-type viruses. Here, we present the complete genome sequences and biological characterization of two MSV isolates from wheat that share approximately 89% sequence identity with the maize-type viruses. Clonal populations of these two isolates, named MSV-Tas and MSV-VW, were leafhopper-transmitted to Digitaria sanguinalis and a range of maize, wheat, and barley genotypes. Whereas the two viruses showed some differences in their pathogenicity in maize, they were both equally pathogenic in D. sanguinalis and the various wheat and barley genotypes tested. Phylogenetic analyses involving the genome sequences of MSV-Tas and MSV-VW, a new maize-type virus also fully sequenced in this study (MSV-VM), and all other available African streak virus sequences, indicated that MSV-Tas and MSV-VW are close relatives that together represent a distinct MSV strain. Sequence analyses revealed that MSV-VM has a recombinant genome containing MSV-Tas/VW-like sequences within its movement protein gene.

Sequence diversity and virulence in Zea mays of Maize streak virus isolates

Full genomic sequences were determined for 12 Maize streak virus (MSV) isolates obtained from Zea mays and wild grass species. These and 10 other publicly available full-length sequences were used to classify a total of 66 additional MSV isolates that had been characterized by PCR-restriction fragment length polymorphism and/or partial nucleotide sequence analysis. A description is given of the host and geographical distribution of the MSV strain and subtype groupings identified. The relationship between the genotypes of 21 fully sequenced virus isolates and their virulence in differentially MSV-resistant Z. mays genotypes was examined. Within the only MSV strain grouping that produced severe symptoms in maize, highly virulent and widely distributed genotypes were identified that are likely to pose the most serious threat to maize production in Africa. Evidence is presented that certain of the isolates investigated may be the products of either intra- or interspecific recombination.

Improved Efficiency of Zea mays Agroinoculation with Maize streak virus

Agroinoculation is a technique permitting the transmission of geminivirus genomes cloned in Agrobacterium tumefaciens into a wide variety of mono- and dicotyledonous host plants. Most geminiviruses are obligately transmitted by insect vector species under natural conditions; therefore, agroinoculation has greatly simplified the study of this group of viruses. In many cases, agroinoculation has replaced insect transmission, and has been used to compare virulence characteristics among viruses. Here we report on the discovery that, in agroinfectious Maize streak virus constructs, the orientation of cloned viral genomes relative to the Cauliflower mosaic virus 35S (CaMV35S) promoter of the binary cloning vector pBI121 can significantly affect agroinfectivity of the constructs. Rates at which plants became symptomatic were significantly higher when agroinoculating maize seedlings with constructs containing the CaMV35S promoter upstream of the viral replication-associated protein (Rep) gene than when the same viruses were cloned either in the opposite orientation or into a vector without a strong eukaryotic promoter sequence. Plants infected using the construct with Rep cloned downstream of the CaMV35S promoter also displayed more stunting and, in the early stages of the infection, more severe chlorotic streak symptoms.

Evaluation of Maize Streak Virus Pathogenicity in Differentially Resistant Zea mays Genotypes

ABSTRACT We devised a rapid technique for the objective and precise assessment of both the pathogenicity of maize streak virus (MSV) isolates and the MSV resistance of maize genotypes. The technique involves the use of agroinoculation to infect maize seedlings and the objective symptom evaluation by quantification of infection rates, stunting, and chlorotic leaf areas. In assessing the MSV resistance of 19 maize genotypes, we describe how the use of differentially virulent virus isolates enables the analysis of MSV resistance phenotypes, ranging from extremely susceptible to completely immune. We further demonstrate how quantification of chlorotic leaf areas by image analysis permits differentiation between degrees of MSV resistance that are indistinguishable from one another using currently employed symptom assessment approaches. Using chlorotic area measurements, we quantify the virulence of a diverse group of 10 MSV isolates and, through agroinoculation of differentially susceptible maize genotypes, we demonstrate the use of our technique in evaluating the pathogenicity of these isolates.

Screening for Late Blight Susceptibility in Potato Tubers by Digital Analysis of Cut Tuber Surfaces

A method for quantification of late blight (Phytophthora infestans) in potato tuber tissue using a digital scanner and image analysis software is presented. The average reflective intensity of light reflected from the cut surface of sample tubers measures the darkened, diseased potato tuber tissue amid lighter, late blight-free tissue. In the absence of disease, potato variety, tuber shape, and tuber size do not influence the scan results. While digital quantification of late blight in tubers under controlled inoculation conditions is consistent, the digital assessments of late blight did not correspond exactly with those from a conventional subjective visual method. Used together, the methods can provide complementary information regarding varietal susceptibility to P. infestans development on the tuber surface and internal tuber tissue. The method of image analysis presented may be used to determine susceptibility of potato tubers to late blight in varietal development programs, storage research programs, or other tuber research programs.