Data on quantitative resistance of wheat to Septoria tritici blotch

Data consists of 10 columns, first row shows column names. Each subsequent row corresponds to data from an individual leaf. Column 1 - leaf index, column 2 - leaf label, column 3 - leaf area in mm2, column 4 - area covered by necrotic tissue in mm2, column 5 - percentage of leaf area covered by lesions (PLACL), column 6 - number of pycnidia on the leaf, column 7 - mean area of pycnidia on the leaf in mm2, column 8 - number of pycnidia per cm2 leaf, column 9 - number of pycnidia per cm2 lesion, column 10 - pycnidia grey value. Leaf label in column 2 uniquely identifies each leaf in the collection. It consists of three parts divided by underscore symbols "_". First part describes the time point of collection ("c1" - collection t1, 25 May 2016; "c3" - collection t2, 4 July, 2016). Second part is the sowing number that uniquely identifies the small wheat plot planted with a specific wheat cultivar. Third part is the index of a leaf within a specific plot. For example, leaf with the label "c1_sn133_7" comes from collection t1, sowing number 133, leaf index 7

Data on quantitative resistance of wheat to Septoria tritici blotch

Data consists of 10 columns, first row shows column names. Each subsequent row corresponds to data from an individual leaf. Column 1 - leaf index, column 2 - leaf label, column 3 - leaf area in mm2, column 4 - area covered by necrotic tissue in mm2, column 5 - percentage of leaf area covered by lesions (PLACL), column 6 - number of pycnidia on the leaf, column 7 - mean area of pycnidia on the leaf in mm2, column 8 - number of pycnidia per cm2 leaf, column 9 - number of pycnidia per cm2 lesion, column 10 - pycnidia grey value. Leaf label in column 2 uniquely identifies each leaf in the collection. It consists of three parts divided by underscore symbols "_". First part describes the time point of collection ("c1" - collection t1, 25 May 2016; "c3" - collection t2, 4 July, 2016). Second part is the sowing number that uniquely identifies the small wheat plot planted with a specific wheat cultivar. Third part is the index of a leaf within a specific plot. For example, leaf with the label "c1_sn133_7" comes from collection t1, sowing number 133, leaf index 7

Table_1_In-Field Detection and Quantification of Septoria Tritici Blotch in Diverse Wheat Germplasm Using Spectral–Temporal Features.docx

Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral–temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral–temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.</p

Correspondence between sowing numbers and cultivar names

Column 1 - cultivar name, column 2 - corresponding sowing number. Sowing numbers in the range 1-360 belong to Lot 4 (replicate 1), sowing numbers in the range 433-792 belong to Lot 3 (replicate 2)

Correspondence between sowing numbers and cultivar names

Column 1 - cultivar name, column 2 - corresponding sowing number. Sowing numbers in the range 1-360 belong to Lot 4 (replicate 1), sowing numbers in the range 433-792 belong to Lot 3 (replicate 2)

Data_Sheet_1_Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease.docx

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.</p

Data_Sheet_2_Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease.csv

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.</p