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

Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions

Early detection of pathogenic fungi in controlled environment areas can prevent major food production losses. Grey mould caused by Botrytis cinerea is often detected as an infection on lettuce. This paper explores the use of vegetation indices for early detection and monitoring of grey mould on lettuce under different lighting conditions in controlled environment chambers. The aim was focused on the potential of using vegetation indices for the early detection of grey mould and on evaluating their changes during disease development in lettuce grown under different lighting conditions. The experiment took place in controlled environment chambers, where day/night temperatures were 21 ± 2/17 ± 2 °C, a 16 h photoperiod was established, and relative humidity was 70 ± 10% under different lighting conditions: high-pressure sodium (HPS) and light-emitting diode (LED) lamps. Lettuces were inoculated by 7-day-old fungus Botrytis cinerea isolate at the BBCH 21. As a control, non-inoculated lettuces were grown under HPS and LEDs (non-inoculated). Then, the following were evaluated: Anthocyanin Reflectance Index 2 (ARI2); Carotenoid Reflectance Index 2 (CRI2); Structure Intensive Pigment Index (SIPI); Flavanol Reflectance Index (FRI); Greenness (G); Greenness 2 (G2); Redness (R); Blue (B); Blue Green Index 2 (BGI2); Browning Index 2 (BRI2); Lichtenthaler Index 1 (LIC1); Pigment Specific Simple Ratio (PSSRa and PSSRb); Gitelson and Merzlyak (GM1 and GM2); Zarco Tejada-Miller Index (ZMI); Normalized Difference Vegetation Index (NDVI); Simple Ratio (SR); Red-Eye Vegetation Stress Index (RVSI); Photochemical Reflectance Index (PRI); Photochemical Reflectance Index 515 (PRI515); Water Band Index (WBI); specific disease index for individual study (fD); Healthy Index (HI); Plant Senescence Reflectance (PSRI); Vogelmann Red Edge Index (VREI1); Red Edge Normalized Difference Vegetation Index (RENDVI); and Modified Red Edge Simple Ratio (MRESRI). Our results showed that the PSRI and fD vegetation indices significantly detected grey mould on lettuce grown under both lighting systems (HPS and LEDs) the day after inoculation. The results conclusively affirmed that NDVI, PSRI, HI, fD, WBI, RVSI, PRI, PRI515, CRI2, SIPI, chlorophyll index PSSRb, and coloration index B were identified as the best indicators for Botrytis cinerea infection on green-leaf lettuce (Lactuca sativa L. cv Little Gem) at the early stage of inoculated lettuce's antioxidative response against grey mould with a significant increase in chlorophyll indices.

Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques

Tobacco Mosaic Virus (TMV) and Potato Virus Y (PVY) pose significant threats to crop production. Non-destructive and accurate surveillance is crucial to effective disease control. In this study, we propose the adoption of hyperspectral and machine learning technologies to discern the type and severity of tobacco leaves affected by PVY and TMV infection. Initially, we applied three preprocessing methods - Multivariate Scattering Correction (MSC), Standard Normal Variate (SNV), and Savitzky-Golay smoothing filter (SavGol) - to corrected the leaf full-length spectral sheet data (350-2500nm). Subsequently, we employed two classifiers, support vector machine (SVM) and random forest (RF), to establish supervised classification models, including binary classification models (healthy/diseased leaves or PVY/TMV infected leaves) and six-class classification models (healthy and various severity levels of diseased leaves). Based on the core evaluation index, our models achieved accuracies in the range of 91-100% in the binary classification. In general, SVM demonstrated superior performance compared to RF in distinguishing leaves infected with PVY and TMV. Different combinations of preprocessing methods and classifiers have distinct capabilities in the six-class classification. Notably, SavGol united with SVM gave an excellent performance in the identification of different PVY severity levels with 98.1% average precision, and also achieved a high recognition rate (96.2%) in the different TMV severity level classifications. The results further highlighted that the effective wavelengths captured by SVM, 700nm and 1800nm, would be valuable for estimating disease severity levels. Our study underscores the efficacy of integrating hyperspectral technology and machine learning, showcasing their potential for accurate and non-destructive monitoring of plant viral diseases.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (A_F), area of healthy foliage (A_H), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning

Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.

Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R2 = 0.968), PSAD (R2 = 0.882) and NC (R2 = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops.

One stop shop IV: taxonomic update with molecular phylogeny for important phytopathogenic genera: 76–100 (2020)

This is a continuation of a series focused on providing a stable platform for the taxonomy of phytopathogenic fungi and fungus-like organisms. This paper focuses on one family: Erysiphaceae and 24 phytopathogenic genera: Armillaria, Barriopsis, Cercospora, Cladosporium, Clinoconidium, Colletotrichum, Cylindrocladiella, Dothidotthia,, Fomitopsis, Ganoderma, Golovinomyces, Heterobasidium, Meliola, Mucor, Neoerysiphe, Nothophoma, Phellinus, Phytophthora, Pseudoseptoria, Pythium, Rhizopus, Stemphylium, Thyrostroma and Wojnowiciella. Each genus is provided with a taxonomic background, distribution, hosts, disease symptoms, and updated backbone trees. Species confirmed with pathogenicity studies are denoted when data are available. Six of the genera are updated from previous entries as many new species have been described.

Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier

Powdery mildew (PM, Blumeria graminis f. sp. tritici) is a devastating disease for wheat growth and production. It is highly meaningful that the disease severities can be objectively and accurately identified by image visualization technology. In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. The disease severities of wheat leaves infected with PM were quantitatively identified based on hyperspectral images and image segmentation techniques. A technical procedure was proposed to perform the identification and evaluation of leaf-scale wheat PM, specifically including three primary steps of the acquisition and preprocessing of hyperspectral images, the selection of characteristic bands, and model construction. Firstly, three-dimensional reduction algorithms, namely principal component analysis (PCA), random forest (RF), and the successive projections algorithm (SPA), were comparatively used to select the bands that were most sensitive to PM. Then, three diagnosis models were constructed by a support vector machine (SVM), RF, and a probabilistic neural network (PNN). Finally, the best model was selected by comparing the overall accuracies. The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. There was an obvious improvement of the identification accuracy with the model, which achieved an 88.00% accuracy derived from the original hyperspectral images. This study can provide a reference for accurately estimating the disease severity of leaf-scale wheat PM and other plant diseases by non-contact measurement technology.

Rapid Detection of Cercospora beticola in Sugar Beet and Mutations Associated with Fungicide Resistance Using LAMP or Probe-Based qPCR

Cercospora leaf spot (CLS), caused by the fungal pathogen Cercospora beticola, is the most destructive disease of sugar beet worldwide. Although growing CLS-tolerant varieties is helpful, disease management currently requires timely application of fungicides. However, overreliance on fungicides has led to the emergence of fungicide resistance in many C. beticola populations, resulting in multiple epidemics in recent years. Therefore, this study focused on developing a fungicide resistance detection "toolbox" for early detection of C. beticola in sugar beet leaves and mutations associated with different fungicides in the pathogen population. A loop-mediated isothermal amplification (LAMP) method was developed for rapid detection of C. beticola in infected sugar beet leaves. The LAMP primers specific to C. beticola (Cb-LAMP) assay was able to detect C. beticola in inoculated sugar beet leaves as early as 1 day postinoculation. A quinone outside inhibitor (QoI)-LAMP assay was also developed to detect the G143A mutation in cytochrome b associated with QoI resistance in C. beticola. The assay detected the mutation in C. beticola both in vitro and in planta with 100% accuracy. We also developed a probe-based quantitative PCR (qPCR) assay for detecting an E198A mutation in β-tubulin associated with benzimidazole resistance and a probe-based qPCR assay for detection of mutations in cytochrome P450-dependent sterol 14α-demethylase (Cyp51) associated with resistance to sterol demethylation inhibitor fungicides. The primers and probes used in the assay were highly efficient and precise in differentiating the corresponding fungicide-resistant mutants from sensitive wild-type isolates.

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

BACKGROUND

Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging.

RESULTS

Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult.

CONCLUSIONS

In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.

Scoring Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems

Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination. Several variables are extracted from the images (spot density and spot size for UGV, green fraction for UGV and UAV) and related to visual scores assessed by an expert. Results show that spot density and green fraction are critical variables to assess low and high CLS severities, respectively, which emphasizes the importance of having submillimeter images to early detect CLS in field conditions. Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV- and UAV-derived scores. While UGV shows the best estimation performance, UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired. Advantages and limitations of UGV, UAV, and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.

Sensory assessment of Cercospora beticola sporulation for phenotyping the partial disease resistance of sugar beet genotypes

BACKGROUND

Due to its high damaging potential, Cercospora leaf spot (CLS) caused by Cercospora beticola is a continuous threat to sugar beet production worldwide. Breeding for disease resistance is hampered by the quantitative nature of resistance which may result from differences in penetration, colonization, and sporulation of the pathogen on sugar beet genotypes. In particular, problems in the quantitative assessment of C. beticola sporulation have resulted in the common practice to assess field resistance late in the growth period as quantitative resistance parameter. Recently, hyperspectral sensors have shown potential to assess differences in CLS severity. Hyperspectral microscopy was used for the quantification of C. beticola sporulation on sugar beet leaves in order to characterize the host plant suitability / resistance of genotypes for decision-making in breeding for CLS resistance.

RESULTS

Assays with attached and detached leaves demonstrated that vital plant tissue is essential for the full potential of genotypic mechanisms of disease resistance and susceptibility. Spectral information (400 to 900 nm, 160 wavebands) of CLSs recorded before and after induction of C. beticola sporulation allowed the identification of sporulating leaf spot sub-areas. A supervised classification and quantification of sporulation structures was possible, but the necessity of genotype-specific reference spectra restricts the general applicability of this approach. Fungal sporulation could be quantified independent of the host plant genotype by calculating the area under the difference reflection spectrum from hyperspectral imaging before and with sporulation. The overall relationship between sensor-based and visual quantification of C. beticola sporulation on five genotypes differing in CLS resistance was R2 = 0.81; count-based differences among genotypes could be reproduced spectrally.

CONCLUSIONS

For the first time, hyperspectral imaging was successfully tested for the quantification of sporulation as a fungal activity depending on host plant suitability. The potential of this non-invasive and non-destructive approach for the quantification of fungal sporulation in other host-pathogen systems and for the phenotyping of crop traits complex as sporulation resistance is discussed.

Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN)

Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize

Tar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in the maize growing regions of the country. Although remote sensing (RS) techniques are increasingly being used for plant phenotyping, they have not been applied to phenotyping TSC resistance in maize. In this study, several multispectral vegetation indices (VIs) and thermal imaging of maize plots under disease pressure and disease-free conditions were tested using an unmanned aerial vehicle (UAV) over two crop seasons. A strong relationship between grain yield, a vegetative index (MCARI2), and canopy temperature was observed under disease pressure. A strong relationship was also observed between the area under the disease progress curve of TSC and three vegetative indices (RDVI, MCARI1, and MCARI2). In addition, we demonstrated that TSC could cause up to 58% yield loss in the most susceptible maize hybrids. Our results suggest that the RS techniques tested in this study could be used for high throughput phenotyping of TSC resistance and potentially for other foliar diseases of maize. This may help reduce the cost and time required for the development of improved maize germplasm. Challenges and opportunities in the use of RS technologies for disease resistance phenotyping are discussed.

An Evaluation of the Variation in the Morphometric Parameters of Grain of Six Triticum Species with the Use of Digital Image Analysis

Kernel images of six wheat species were subjected to shape and color analyses to determine variations in the morphometric parameters of grain. The values of kernel shape descriptors (area, perimeter, Feret diameter, minimal Feret diameter, circularity, aspect ratio, roundness, solidity) and color descriptors (H, S, I and L*a*b*) were investigated. The influence of grain colonization by endophytic fungi on the color of the seed coat was also evaluated. Polish wheat grain was characterized by the highest intraspecific variation in shape and color. Bread wheat was most homogeneous in terms of the studied shape and color descriptors. An analysis of variations in wheat lines revealed greater differences in phenotypic traits of relict wheats, which have a larger gene pool. The grain of ancient wheat species was characterized by low roundness values and relatively low solidity. Shape and color descriptors were strongly discriminating components in the studied wheat species. Their discriminatory power was determined mainly by genotype. A method that supports rapid discrimination of cereal species and admixtures of other cereals in grain batches is required to guarantee the quality and safety of grain. The results of this study indicate that digital image analysis can be effectively used for this purpose.

Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease

Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance.

New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor's relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI's ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests.

Management of Cercospora Leaf Spot in Conventional and Organic Table Beet Production

Cercospora leaf spot (CLS; Cercospora beticola) is the most important foliar disease affecting table beet. Epidemics occur annually and fungicides extend the survival of foliage to enable mechanized harvest. However, a high frequency of strobilurin-resistant C. beticola isolates necessitates the identification of fungicides with different modes of action for tactical rotation. There is also substantial demand for organically produced table beet, for which synthetic fungicides are prohibited. Five small-plot, replicated field trials were conducted over two years to evaluate conventional and Organic Materials Review Institute (OMRI)-listed products for CLS control in table beet cv. Ruby Queen at Geneva and Ithaca, New York. Benzovindiflupyr + difenoconazole significantly reduced temporal disease progress (measured by the area under the disease progress stairs; AUDPS) by 86.7 to 97.3% compared with nontreated plots, and mean survival time of leaves was significantly extended. The demethylation inhibitor, propiconazole, also provided significant disease control in two trials in 2016. Disease severity in plots treated with succinate dehydrogenase inhibitors (boscalid, fluxapyroxad + pyraclostrobin, and penthiopyrad) was significantly decreased compared with nontreated plots but less than other fungicides. Efficacious fungicides significantly increased the dry weight of foliage but did not significantly affect the dry weight of roots, and root shoulder diameter. The enhanced longevity of leaves and increased dry weight of foliage may extend opportunities for mechanized harvesting without deleteriously affecting root yield parameters which are strictly regulated for the processing markets. In two trials, copper octanoate + Bacillus amyloliquefaciens strain D747 (as Cueva + Double Nickel LC) resulted in significantly improved disease control in comparison with application of either product alone and provided comparable and reproducible disease control equivalent to conventional fungicides at both locations. The implications of these findings for CLS control in conventional and organic table beet production systems are discussed.

Electron Paramagnetic Resonance (EPR) Spectroscopy in Studies of the Protective Effects of 24-Epibrasinoide and Selenium against Zearalenone-Stimulation of the Oxidative Stress in Germinating Grains of Wheat

These studies concentrate on the possibility of using selenium ions and/or 24-epibrassinolide at non-toxic levels as protectors of wheat plants against zearalenone, which is a common and widespread mycotoxin. Analysis using the UHPLC-MS technique allowed for identification of grains having the stress-tolerant and stress-sensitive wheat genotype. When germinating in the presence of 30 µM of zearalenone, this mycotoxin can accumulate in both grains and hypocotyls germinating from these grains. Selenium ions (10 µM) and 24-epibrassinolide (0.1 µM) introduced together with zearalenone decreased the uptake of zearalenone from about 295 to 200 ng/g and from about 350 to 300 ng/g in the grains of tolerant and sensitive genotypes, respectively. As a consequence, this also resulted in a reduction in the uptake of zearalenone from about 100 to 80 ng/g and from about 155 to 128 ng/g in the hypocotyls from the germinated grains of tolerant and sensitive wheat, respectively. In the mechanism of protection against the zearalenone-induced oxidative stress, the antioxidative enzymes-mainly superoxide dismutase (SOD) and catalase (CAT)-were engaged, especially in the sensitive genotype. Electron paramagnetic resonance (EPR) studies allowed for a description of the chemical character of the long-lived organic radicals formed in biomolecular structures which are able to stabilize electrons released from reactive oxygen species as well as the changes in the status of transition paramagnetic metal ions. The presence of zearalenone drastically decreased the amount of paramagnetic metal ions-mainly Mn(II) and Fe(III)-bonded in the organic matrix. This effect was particularly found in the sensitive genotype, in which these species were found at a smaller level. The protective effect of selenium ions and 24-epibrassinolide originated from their ability to inhibit the destruction of biomolecules by reactive oxygen species. An increased ability to defend biomolecules against zearalenone action was observed for 24-epibrassinolide.

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%).

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.

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.

Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases

Hyperspectral imaging (HSI) offers high potential as a non-invasive diagnostic tool for disease detection. In this paper leaf characteristics and spectral reflectance of sugar beet leaves diseased with Cercospora leaf spot, powdery mildew and leaf rust at different development stages were connected. Light microscopy was used to describe the morphological changes in the host tissue due to pathogen colonisation. Under controlled conditions a hyperspectral imaging line scanning spectrometer (ImSpector V10E) with a spectral resolution of 2.8 nm from 400 to 1000 nm and a spatial resolution of 0.19 mm was used for continuous screening and monitoring of disease symptoms during pathogenesis. A pixel-wise mapping of spectral reflectance in the visible and near-infrared range enabled the detection and detailed description of diseased tissue on the leaf level. Leaf structure was linked to leaf spectral reflectance patterns. Depending on the interaction with the host tissue, the pathogens caused disease-specific spectral signatures. The influence of the pathogens on leaf reflectance was a function of the developmental stage of the disease and of the subarea of the symptoms. Spectral reflectance in combination with Spectral Angle Mapper classification allowed for the differentiation of mature symptoms into zones displaying all ontogenetic stages from young to mature symptoms. Due to a pixel-wise extraction of pure spectral signatures a better understanding of changes in leaf reflectance caused by plant diseases was achieved using HSI. This technology considerably improves the sensitivity and specificity of hyperspectrometry in proximal sensing of plant diseases.

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.

Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

Estimating mycotoxin contents of Fusarium-damaged winter wheat kernels

Winter wheat (Triticum aestivum L., cultivars Ritmo and Dekan) grain was sampled in Northern Germany between 2001 and 2006. Kernels damaged by fungi of the genus Fusarium were separated from sound grain by visual assessment. Samples containing 0%, 20%, 40%, 60%, 80% and 100% of Fusarium-damaged kernels were compiled and analyzed for the Fusarium type B trichothecenes deoxynivalenol (DON, 2001-2006), nivalenol (NIV, 2006), 3-acetyl-deoxynivalenol (3AcDON, 2006) and 15-acetyl-deoxynivalenol (15AcDON, 2006). The relationship between mycotoxin contents and the percentage of Fusarium-damaged kernels was calculated for each lot of grain. Apart from one exception, relationships between the percentage of Fusarium-damaged kernels and NIV, 3AcDON or 15AcDON were non-significant. In contrast, close relationships between the percentage of Fusarium-damaged kernels and the DON content were observed (r(2)=0.93-0.99). The y-axis intercepts were not significantly different from zero, but the DON content of the damaged kernels varied by a factor of 11.59 between years and by a factor of 1.87 between cultivars. Fusarium-damaged kernels contained between 0.21 and 2.39 microg DON kernel(-1). The overall average DON content of a Fusarium-damaged wheat kernel was 1.29 +/- 0.11 microg. The DON content of diseased kernels was affected by environment and wheat genotype but not by genotype x environment interaction. On average, Fusarium-damaged kernels contained 9.7-fold more DON than 15AcDON, 19.5-fold more DON than NIV, and 26.9-fold more DON than 3AcDON. 3AcDON and 15AcDON contents per wheat kernel were not significantly different between cultivars. On average, 4.27% of Fusarium-damaged kernels were sufficient to reach the 1.25 mg DON kg(-1) grain limit for unprocessed cereals in the EU. Given the low percentages of Fusarium-damaged kernels that are equivalent to current legal DON limits, grading accuracies >96% would be needed when using automatic grading systems for separating sound from damaged kernels.

Use of a Multispectral Radiometer for Noninvasive Assessments of Foliar Disease Caused by Ray Blight in Pyrethrum

Foliar disease due to ray blight (Phoma ligulicola) in pyrethrum was quantified at three locations over 2 years in Tasmania, Australia. To obtain a range of ray blight disease intensities, replicated plots were treated with fungicides that varied in efficacy to control ray blight. Visual disease assessments and measurement of canopy reflectance were made at least once during spring (September through December). Visual assessments involved removal of flowering stems at ground level from which measurements of defoliation severity and the incidence of stems with ray blight were obtained. Reflectance of sunlight from pyrethrum canopies was measured at 485, 560, 660, 830, and 1,650 nm using a handheld multispectral radiometer. Measurements from these wavelengths also were used to calculate all possible reflectance ratios, as well as four vegetative indices. Relationships between wavelength bands, reflectance ratios, vegetative indices, and disease intensity measures were described by linear regression analyses. Several wavelength bands, ratios, and vegetative indices were significantly related in a linear fashion to visual measures of disease intensity. The most consistent relationships, with high R² and low coefficients of variation values, varied with crop growth stage over time. The ratio 830/560 was identified as the best predictor of stem height, defoliation severity, and number of flowers produced on each stem in October. However, reflectance within the near-infrared range (830 nm) and the difference vegetative index was superior in November. The use of radiometric assessment of disease was noninvasive and provided savings in disease assessment time, which is critical where visual assessment is difficult and requires destructive sampling, as with pyrethrum.

Quantifying the Effects of Lance Nematode Parasitism in Creeping Bentgrass

We compared photosynthesis and multispectral radiometry (MSR) measurements with visual quality ratings for assessment of feeding injury to creeping bentgrass caused by the lance nematode (Hoplolaimus galeatus) using artificially infested microplots and a naturally infested putting green. Nematode feeding resulted in negative visual and MSR effects on creeping bentgrass in microplots. Visual quality ratings were correlated more consistently with nematode densities than either individual MSR variables or factor models of MSR variables. Threshold estimates for H. galeatus population densities associated with unacceptable bentgrass quality in microplots varied widely by month and year. Similarly, the relationship between H. galeatus population density and turf health indicators (including MSR measurements, visual ratings, and net photosynthetic rate) varied with cultivar and management practice (irrigation frequency and mowing height) in the naturally infested putting green. Notably, negative effects of nematode feeding were not consistently associated with more stressful management practices, suggesting that stress avoidance is not a reliable deterrent to H. galeatus damage in creeping bentgrass. Damage thresholds for this nematode-host association are dynamic and should be used with caution.