Early detection of white mold caused by Sclerotinia sclerotiorum in potato fields using real-time PCR

Genetic diversity studies based on morpho-pathological and molecular variability of the Sclerotinia sclerotiorum population infecting potato (Solanum tuberosum L.)

White mould or stem rot, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is a devastating fungal disease found in major potato cultivation areas worldwide. The aim of this study was to characterize genetic diversity in the S. sclerotiorum population from the main potato producing regions of India by means of morphological (mycelial growth, colony colour, number and distribution pattern of sclerotia) and molecular characteristics, as well as to evaluate the virulence of S. sclerotiorum isolates in potato for the first time. Among the S. sclerotiorum population analyzed, high phenotypic and genotypic diversity were observed. Using all the morphological characteristics, a dendrogram was constructed based on Gower's similarity coefficient that distributed all the isolates into three clusters at the 0.62 similarity coefficient. Carpogenic germination of apothecia revealed that larger sclerotia produced a greater number of apothecia while smaller sclerotia produced fewer apothecia. Pathogenicity test results revealed that out of 25 isolates, seven were highly aggressive, 14 were moderate and four had low aggressiveness, whilst isolates from Punjab were more pathogenic than those of Uttar Pradesh. Phylogenetic analysis of universal rice primer polymorphism showed high genetic variability within the isolates that grouped all the isolates in three evolutionary lineages in the resulting dendrogram and showed partial relationship with geographical locations of the isolates. Further, the findings suggest the occurrence of higher heterogeneity and genetic diversity among the S. sclerotiorum isolates that indicates the existence of both clonal and sexual reproduction in the pathogen population of potato producing areas in India.

Detection of Sclerotinia Stem Rot on Oilseed Rape (Brassica napus L.) Leaves Using Hyperspectral Imaging

Hyperspectral imaging was explored to detect Sclerotinia stem rot (SSR) on oilseed rape leaves with chemometric methods, and the influences of variable selection, machine learning, and calibration transfer methods on detection performances were evaluated. Three different sample sets containing healthy and infected oilseed rape leaves were acquired under different imaging acquisition parameters. Four discriminant models were built using full spectra, including partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and k-nearest neighbors (KNN). PLS-DA and SVM models were also built with the optimal wavelengths selected by principal component analysis (PCA) loadings, second derivative spectra, competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA). The optimal wavelengths selected for each sample set by different methods were different; however, the optimal wavelengths selected by PCA loadings and second derivative spectra showed similarity between different sample sets. Direct standardization (DS) was successfully applied to reduce spectral differences among different sample sets. Overall, the results demonstrated that using hyperspectral imaging with chemometrics for plant disease detection can be efficient and will also help in the selection of optimal variable selection, machine learning, and calibration transfer methods for fast and accurate plant disease detection.

Evaluation of carbon nanotube based copper nanoparticle composite for the efficient detection of agroviruses

We report a biosensor that combines the construction of a three-dimensional nanocomposite with electrochemical methods for the detection of viruses in plants. This is the first report, where carbon nanotubes are used as a conductive frame to anchor highly electrolytic agglomerates of copper nanoparticles to detect agroviruses. Morphological analysis of nanocomposite revealed the presence of carbon nanotubes having a diameter of 50-100nm with copper nanoparticles of 20-100nm, attached in the form of bunches. This material was applied to assess the infection caused by geminiviruses which are a major threat to the cotton plants in Asian and African countries. The hybridization events were studied by monitoring differential pulse voltammetry signals using methylene blue as a redox indicator. In the presence of target DNA, sensor signals decreased from 7×10^-4 to 1×10^-4Ampere. The probe exhibited 97.14% selectivity and the detection limit was found to be 0.01ngμL^-1. The developed biosensor is stable for at least four weeks, losing only 4.3% of the initial signal value. This sensor was able to detect the presence of viruses in sap extracted from cotton leaves, thus providing a promising platform to detect a range of other crops-infecting viruses.

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Hyperspectral imaging covering the spectral range of 384-1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems.