RESUMO
BACKGROUND: Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. RESULTS: Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. CONCLUSIONS: An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.
RESUMO
Powdery mildew of grapevines is difficult to assess visually at the weighbridge, particularly in large consignments of machine-harvested fruit. To facilitate accurate methods for the detection and quantification of the disease in grape samples obtained from both the vineyard and winery, we developed a DNA probe for the pathogen Erysiphe necator. The E. necator-specific 450 bp DNA fragment pEnA1, targets highly repetitive sequences and was isolated from a partial genomic library. In screening for species specificity, clone pEnA1 was used in slot-blot hybridization and detected E. necator DNA from grapes and resultant must and juice, but not from clarified juice and wine. The detection threshold was approximately 50 pg of E. necator DNA per 100 ng total DNA of grape sample and was equivalent to 1-5% of a grape bunch visually affected by powdery mildew. Disease severity, expressed as the percentage of surface area of a bunch with powdery mildew, and E. necator DNA content were highly correlated, r2=0.955, P<0.001. The DNA-based hybridization assay has the potential to predict the severity of powdery mildew in grape samples from the vineyard and in must and juice samples at the winery. The DNA sequence of clone pEnA1 was used to design species-specific primers, the results maintaining the same specificity patterns observed in the initial hybridization assays. The PCR-based assay was sensitive enough to detect approximately 1 pg DNA, being equivalent to 1 conidium per sample. This is the first report to date of the detection of all known phenetic groups of E. necator DNA and of the quantification of DNA from grape samples at the winery. Accurate information on the extent of powdery mildew contamination of grape lots would enable wineries to make more informed decisions about the use of fruit and must.