Remote detection of rhizomania in sugar beets

Plant Viral Disease Detection: From Molecular Diagnosis to Optical Sensing Technology—A Multidisciplinary Review

Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed.

The Optical Response of a Mediterranean Shrubland to Climate Change: Hyperspectral Reflectance Measurements during Spring

Remote sensing techniques in terms of monitoring plants’ responses to environmental constraints have gained much attention during recent decades. Among these constraints, climate change appears to be one of the major challenges in the Mediterranean region. In this study, the main goal was to determine how field spectrometry could improve remote sensing study of a Mediterranean shrubland submitted to climate aridification. We provided the spectral signature of three common plants of the Mediterranean garrigue: Cistus albidus, Quercus coccifera, and Rosmarinus officinalis. The pattern of these spectra changed depending on the presence of a neighboring plant species and water availability. Indeed, the normalized water absorption reflectance (R975/R900) tended to decrease for each species in trispecific associations (11–26%). This clearly indicates that multispecific plant communities will better resist climate aridification compared to monospecific stands. While Q. coccifera seemed to be more sensible to competition for water resources, C. albidus exhibited a facilitation effect on R. officinalis in trispecific assemblage. Among the 17 vegetation indices tested, we found that the pigment pheophytinization index (NPQI) was a relevant parameter to characterize plant–plant coexistence. This work also showed that some vegetation indices known as indicators of water and pigment contents could also discriminate plant associations, namely RGR (Red Green Ratio), WI (Water Index), Red Edge Model, NDWI1240 (Normalized Difference Water Index), and PRI (Photochemical Reflectance Index). The latter was shown to be linearly and negatively correlated to the ratio of R975/R900, an indicator of water status.

Field-based remote sensing models predict radiation use efficiency in wheat

Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.

Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables

Research has been increasingly focusing on the selection of novel and effective biological control agents (BCAs) against soil-borne plant pathogens. The large-scale application of BCAs requires fast and robust screening methods for the evaluation of the efficacy of high numbers of candidates. In this context, the digital technologies can be applied not only for early disease detection but also for rapid performance analyses of BCAs. The present study investigates the ability of different Trichoderma spp. to contain the development of main baby-leaf vegetable pathogens and applies functional plant imaging to select the best performing antagonists against multiple pathosystems. Specifically, sixteen different Trichoderma spp. strains were characterized both in vivo and in vitro for their ability to contain R. solani, S. sclerotiorum and S. rolfsii development. All Trichoderma spp. showed, in vitro significant radial growth inhibition of the target phytopathogens. Furthermore, biocontrol trials were performed on wild rocket, green and red baby lettuces infected, respectively, with R. solani, S. sclerotiorum and S. rolfsii. The plant status was monitored by using hyperspectral imaging. Two strains, Tl35 and Ta56, belonging to T. longibrachiatum and T. atroviride species, significantly reduced disease incidence and severity (DI and DSI) in the three pathosystems. Vegetation indices, calculated on the hyperspectral data extracted from the images of plant-Trichoderma-pathogen interaction, proved to be suitable to refer about the plant health status. Four of them (OSAVI, SAVI, TSAVI and TVI) were found informative for all the pathosystems analyzed, resulting closely correlated to DSI according to significant changes in the spectral signatures among health, infected and bio-protected plants. Findings clearly indicate the possibility to promote sustainable disease management of crops by applying digital plant imaging as large-scale screening method of BCAs' effectiveness and precision biological control support.

Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model

Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g·m−2 for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 − 0.64%; CND: RMSE = 1.26 − 1.78 g·m−2). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat.

Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice

Non-destructive and rapid estimation of canopy variables is imperative for predicting crop growth and managing nitrogen (N) application. Hyperspectral remote sensing can be used for timely and accurate estimation of canopy physical and chemical properties; however, discrepancies associated with soil and water backgrounds complicate the estimation of crop N status using canopy spectral reflectance (CSR). This study established the quantitative relationships between dynamic canopy nitrogen (CN) status indicators, leaf dry weight (LDW), leaf N concentration (LNC), leaf N accumulation (LNA), and CSR-derived new hyperspectral vegetation indices (HVIs), and to access the plausibility of using these relationships to make in-season estimations of CN variables at the elongation (EL), booting (BT), and heading (HD) stages of rice crop growth. Two-year multi-N rate field experiments were conducted in 2015 and 2016 in Hubei Province, China, using the rice cultivar Japonica. The results showed that the sensitive spectral regions were negatively correlated with CN variables in the visible (400-720 nm and 560-710 nm) regions, and positively correlated (r > 0.50, r > 0.60) with red and NIR (720-900 nm) regions. These sensitive regions are used to formulate the new (SR_777/759, SR_768/750) HVIs to predict CN variables at the EL, BT, and HD stages. The newly developed stepwise multiple linear regression (SMLR) models could efficiently estimate the dynamic LDW at the BT stage and LNC and LNA at the HD stage. The SMLR models performed accurately and robustly when used with a validation data set. The projected results offer a suitable approach for rapid and accurate estimation of canopy N-indices for the precise management of N application during the rice growth period.

Canopy Vegetation Indices from In situ Hyperspectral Data to Assess Plant Water Status of Winter Wheat under Powdery Mildew Stress

Plant disease and pests influence the physiological state and restricts the healthy growth of crops. Physiological measurements are considered the most accurate way of assessing plant health status. In this paper, we researched the use of an in situ hyperspectral remote sensor to detect plant water status in winter wheat infected with powdery mildew. Using a diseased nursery field and artificially inoculated open field experiments, we detected the canopy spectra of wheat at different developmental stages and under different degrees of disease severity. At the same time, destructive sampling was carried out for physical tests to investigate the change of physiological parameters under the condition of disease. Selected vegetation indices (VIs) were mostly comprised of green bands, and correlation coefficients between these common VIs and plant water content (PWC) were generally 0.784-0.902 (p < 0.001), indicating the green waveband may have great potential in the evaluation of water content of winter wheat under powdery mildew stress. The Photochemical Reflectance Index (PRI) was sensitive to physiological response influenced by powdery mildew, and the relationships of PRI with chlorophyll content, the maximum quantum efficiency of PSII photochemistry (Fv/Fm), and the potential activity of PSII photochemistry (Fv/Fo) were good with R2 = 0.639, 0.833, 0.808, respectively. Linear regressions showed PRI demonstrated a steady relationship with PWC across different growth conditions, with R2 = 0.817 and RMSE = 2.17. The acquired PRI model of wheat under the powdery mildew stress has a good compatibility to different experimental fields from booting stage to filling stage compared with the traditional water signal vegetation indices, WBI, FWBI1, and FWBI2. The verification results with independent data showed that PRI still performed better with R2 = 0.819 between measured and predicted, and corresponding RE = 8.26%. Thus, PRI is recommended as a potentially reliable indicator of PWC in winter wheat with powdery mildew stress. The results will help to understand the physical state of the plant, and provide technical support for disease control using remote sensing during wheat production.

Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-Resolution Spectral Reflectance Data on Plant Breeding

This article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data. Spectral reflectance data can suffer from problems such as poor signal to noise ratios in various wavebands or invalid measurements due to changes in incoming solar radiation or operator fatigue leading to poor orientation of sensors. Thus, exploratory data analysis is essential to identify appropriate data for further analyses. This software overcomes the problem that analysis tools such as Excel are cumbersome to use for the high number of wavelengths and samples typically acquired in these studies. The software, Spectral Knowledge (SK-UTALCA), was initially developed for plant breeding, but it is also suitable for other studies such as precision agriculture, crop protection, ecophysiology plant nutrition, and soil fertility. Various spectral reflectance indices (SRIs) are often used to relate crop characteristics to spectral data and the software is loaded with 255 SRIs which can be applied quickly to the data. This article describes the architecture and functions of SK-UTALCA and the features of the data that led to the development of each of its modules.

Fungal disease detection in plants: Traditional assays, novel diagnostic techniques and biosensors

Fungal diseases in commercially important plants results in a significant reduction in both quality and yield, often leading to the loss of an entire plant. In order to minimize the losses, it is essential to detect and identify the pathogens at an early stage. Early detection and accurate identification of pathogens can control the spread of infection. The present article provides a comprehensive overview of conventional methods, current trends and advances in fungal pathogen detection with an emphasis on biosensors. Traditional techniques are the "gold standard" in fungal detection which relies on symptoms, culture-based, morphological observation and biochemical identifications. In recent times, with the advancement of biotechnology, molecular and immunological approaches have revolutionized fungal disease detection. But the drawback lies in the fact that these methods require specific and expensive equipments. Thus, there is an urgent need for rapid, reliable, sensitive, cost effective and easy to use diagnostic methods for fungal pathogen detection. Biosensors would become a promising and attractive alternative, but they still have to be subjected to some modifications, improvements and proper validation for on-field use.

Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping

Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.

Mid-Infrared (MIR) and Near-Infrared (NIR) Detection of Rhizoctonia solani AG 2-2 IIIB on Barley-Based Artificial Inoculum

The amount of Rhizoctonia solani in the soil and how much must be present to cause disease in sugar beet (Beta vulgaris L.) is relatively unknown. This is mostly because of the usually low inoculum densities found naturally in soil and the low sensitivity of traditional serial dilution assays. We investigated the usefulness of Fourier transform mid-infrared (MIR) and near-infrared (NIR) spectroscopic properties in identifying the artificial colonization of barley grains with R. solani AG 2-2 IIIB and in detecting R. solani populations in plant tissues and inoculants. The objectives of this study were to compare the ability of traditional plating assays to NIR and MIR spectroscopies to identify R. solani in different-size fractions of colonized ground barley (used as an artificial inoculum) and to differentiate colonized from non-inoculated barley. We found that NIR and MIR spectroscopies were sensitive in resolving different barley particle sizes, with particles that were <0.25 and 0.25-0.5 mm having different spectral properties than coarser particles. Moreover, we found that barley colonized with R. solani had different MIR spectral properties than the non-inoculated samples for the larger fractions (0.5-1.0, 1.0-2.0, and >2.0 mm) of the ground barley. This colonization was confirmed using traditional plating assays. Comparisons with the spectra from pure fungal cultures and non-inoculated barley suggest that the MIR spectrum of colonized barley is different because of the consumption of C substrates by the fungus rather than because of the presence of fungal bands in the spectra of the colonized samples. We found that MIR was better than NIR spectroscopy in differentiating the colonized from the control samples.

Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria

In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%).

Detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance

To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009-2010 and 2010-2011 seasons. Reflectance in near infrared (NIR) regions was significantly correlated with disease index at GS 10.5.3, 10.5.4, and 11.1 at two plant densities in both seasons. For the two plant densities, the area of the red edge peak (Σdr680-760 nm), difference vegetation index (DVI), and triangular vegetation index (TVI) were significantly correlated negatively with disease index at three GSs in two seasons. Compared with other parameters Σdr680-760 nm was the most sensitive parameter for detecting powdery mildew. Linear regression models relating mildew severity to Σdr680-760 nm were constructed at three GSs in two seasons for the two plant densities, demonstrating no significant difference in the slope estimates between the two plant densities at three GSs. Σdr680-760 nm was correlated with grain yield at three GSs in two seasons. The accuracies of partial least square regression (PLSR) models were consistently higher than those of models based on Σdr680760 nm for disease index and grain yield. PLSR can, therefore, provide more accurate estimation of disease index of wheat powdery mildew and grain yield using canopy reflectance.

Reflectance variation within the in-chlorophyll centre waveband for robust retrieval of leaf chlorophyll content

The in-chlorophyll centre waveband (ICCW) (640–680 nm) is the specific chlorophyll (Chl) absorption band, but the reflectance in this band has not been used as an optimal index for non-destructive determination of plant Chl content in recent decades. This study develops a new spectral index based solely on the ICCW for robust retrieval of leaf Chl content for the first time. A glasshouse experiment for solution-culture of one chlorophyll-deficient rice mutant and six wild types of rice genotypes was conducted, and the leaf reflectance (400–900 nm) was measured with a high spectral resolution (1 nm) spectrophotometer and the contents of chlorophyll a (Chla), chlorophyll b (Chlb) and chlorophyll a+b (Chlt) of the rice leaves were determined. It was found that the reflectance curves from 640 nm to 674 nm and from 675 nm to 680 nm of the low-chlorophyll mutant leaf were drastically steeper than that of the wild types in the ICCW. The new index based on the reflectance variation within ICCW, the difference of the first derivative sum within the ICCW (DFDS_ICCW), was highly sensitive (r = −0.77, n = 93, P<0.01) to Chlt while the mean reflectance (R_ICCW) in the ICCW became insensitive (r = −0.12, n = 93, P>0.05) to Chlt when the leaf Chlt was higher than 200 mg/m². The best equations of R-ICCW and DFDS_ICCW yielded an RMSE of 78.7, 32.9 and 107.3 mg/m², and an RMSE of 37.4, 16.0 and 45.3 mg/m⁻², respectively, for predicting Chla, Chlb and Chlt. The new index could rank in the top 10 for prediction of Chla and Chlt as compared with the 55 existing indices. Additionally, most of the 55 existing Chl-related VIs performed robustly or strongly in simultaneous prediction of leaf Chla, Chlb and Chlt.

Remote Sensing for Assessing Rhizoctonia Crown and Root Rot Severity in Sugar Beet

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani AG-2-2, is an increasingly important disease of sugar beet in Minnesota and North Dakota. Disease ratings are based on subjective, visual estimates of root rot severity (0-to-7 scale, where 0 = healthy and 7 = 100% rotted, foliage dead). Remote sensing was evaluated as an alternative method to assess RCRR. Field plots of sugar beet were inoculated with R. solani AG 2-2 IIIB at different inoculum densities at the 10-leaf stage in 2008 and 2009. Data were collected for (i) hyperspectral reflectance from the sugar beet canopy and (ii) visual ratings of RCRR in 2008 at 2, 4, 6, and 8 weeks after inoculation (WAI) and in 2009 at 2, 3, 5, and 9 WAI. Green, red, and near-infrared reflectance and several calculated narrowband and wideband vegetation indices (VIs) were correlated with visual RCRR ratings, and all resulted in strong nonlinear regressions. Values of VIs were constant until at least 26 to 50% of the root surface was rotted (RCRR = 4, wilting of foliage starting to develop) and then decreased significantly as RCRR ratings increased and plants began dying. RCRR also was detected using airborne, color-infrared imagery at 0.25- and 1-m resolution. Remote sensing can detect RCRR but not before initial appearance of foliar symptoms.

Satellite Remote Sensing of Wheat Infected by Wheat streak mosaic virus

The prevalence of wheat streak mosaic, caused by Wheat streak mosaic virus, was assessed using Landsat 5 Thematic Mapper (TM) images in two counties of the Texas Panhandle during the 2005-2006 and 2007-2008 crop years. In both crop years, wheat streak mosaic was widely distributed in the counties studied. Healthy and diseased wheat were separated on the images using the maximum likelihood classifier. The overall classification accuracies were between 89.47 and 99.07% for disease detection when compared to "ground truth" field observations. Omission errors (i.e., pixels incorrectly excluded from a particular class and assigned to other classes) varied between 0 and 12.50%. Commission errors (i.e., pixels incorrectly assigned to a particular class that actually belong to other classes) ranged from 0 to 23.81%. There were substantial differences between planted wheat acreage reported by the United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) and that detected by image analyses. However, harvested wheat acreage reported by USDA-NASS and that detected by image classifications were closely matched. These results indicate that the TM image can be used to accurately detect and quantify incidence of wheat streak mosaic over large areas. This method appears to be one of the best currently available for identification and mapping disease incidence over large and remote areas by offering a repeatable, inexpensive, and synoptic strategy during the course of a growing season.

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary.

Comparison of Visual and Multispectral Radiometric Disease Evaluations of Cercospora Leaf Spot of Sugar Beet

Visual assessments of disease severity are currently the industry standard for quantification of the necrosis caused by Cercospora beticola on sugar beet (Beta vulgaris) leaves. We compared the precision, reproducibility, and sensitivity of a multispectral radiometer to visual disease assessments. Individual wavebands from the radiometer, as well as vegetative indices calculated from the individual wavebands, were compared with visual disease estimates from two raters at each of two research sites. Visual assessments and radiometric measurements were partially repeated immediately after the first assessment at each site. Precision, as measured by reduced coefficients of variation, was improved for all vegetative indices and individual waveband radiometric measures compared with visual assessments. Visual assessments, near-infrared singlewaveband reflectance values, and four of the six vegetative indices had high treatment F values, suggesting greater sensitivity at discriminating disease severity levels. Reproducibility, as measured by a test-retest method, was high for visual assessments, single-waveband reflectance at 810 nm, and several of the vegetative indices. The use of radiometric methods has the potential to increase the precision of assessments of Cercospora leaf spot foliar symptoms of sugar beet while eliminating potential bias. We recommend this method be used in conjunction with visual disease assessments to improve precision of assessments and guard against potential bias in evaluations.

Ecology and epidemiology of benyviruses and plasmodiophorid vectors

Beet necrotic yellow vein virus (BNYVV) and Beet soilborne mosaic virus (BSBMV) are members of the genus Benyvirus, and Burdock mottle virus (BdMV) is a tentative member. BNYVV and BSBMV are vectored by the plasmodiophorid Polymyxa betae, which has a worldwide distribution. Polymyxa betae is morphologically indistinguishable from P. graminis, but recent molecular studies support separation of the two species. The geographic distribution of BNYVV is also worldwide, but BSBMV has been identified only in the United States. In Europe and Japan, several genotypic strains of BNYVV have been identified, and those with a fifth RNA appear to be more aggressive. No thorough survey of genotypic variability of BNYVV or BSBMV has been conducted in the United States. However, both viruses are widespread and frequently found in the same field, infecting the same beet plant. The implications of this close proximity, with regard to disease incidence and severity, and for recombination, are uncertain. Recent technological advances that permit improved detection and quantification of these viruses and their vector offer tremendous research opportunities.