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)

Plant Image Segmentation: Reference Images Wheat

This is a collection of segmentation reference images for the validation of image segmentation accuracy in plant phenotyping research. This collection contains 40 original wheat plant images taken in the field, and their hand-segmented resulting images using Photoshop, i.e. 80 image files in this share. The original images are identified with unique filenames, and the reference images are identified accordingly with the suffix "_Hand" at the end of filenames. <br><br>This collection will be further developed in order to include more phenotyping images of diverse plant species. Therefore, contribution or suggestion to this collection is highly appreciated.<br

DataSheet_1_Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm.docx

The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had little effect. Visually scored delayed senescence was positively correlated with GY ranging from r = 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.</p

Fig 2 -

Manhattan plots for esterase (A, C) and lipase (B, D) activity of the GABI-panel harvested in 2015 (A, B) and 2016 (C, D). The x-axis shows the relative position across 7 chromosomes and the y-axis the -log10P-value. The three genomes, A, B and D, are represented by black, orange and blue, respectively. Significant line is drawn at FDR p = 0.02.</p

MOESM8 of RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion

Additional file 8. Increase in crown root length of 24 genotypes under low nitrogen after the start of the split root treatment

MOESM6 of RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion

Additional file 6. Correlation coefficients (Pearson’s r) of traits. Correlation was done based on best linear unbiased estimates (BLUPS). Abbreviations: Number of lateral roots in the first segment (NoLat 1st) or second segment (NoLat 2nd), length of representative lateral root in the first segment (MedLat 1st) or second segment (MedLat 2nd), maximal lateral root length in the first segment (MaxLat 1st) or in the second segment (MaxLat 2nd), branching density in the first segment (BrLat1st) or in the second segment (BrLat2nd), branching density across both segments (BrLatTot), length of the branching zone (LBrZone), total number of lateral roots (NoLatTot), elongation rate crown roots (ERCr), length of crown roots at solution change (intercept; ICCr), embryonic root dry weight (DWER), crown root dry weight (DWCR), leaf greenness (SPAD), leaf area measured (LAm), dry weight shoot (DWS), leaf area pixel based (LAPix), shoot pixel count at solution change intercept (ICS), shoot pixel count development (ERS), N content in the leaf in % of total dry weight (N). Significance level: ≤ 0.001 ***, ≤ 0.01**, ≤ 0.05*

MOESM7 of RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion

Additional file 7. Increase in crown root length of 24 genotypes under high nitrogen after the start of the split root treatment

MOESM5 of RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion

Additional file 5. A) Best linear unbiased prediction (BLUPS) of parameters with significant treatment effect, but no genotype effect or genotype: treatment interaction. B) Prediction of mean values of parameters with significant genotype effect, but no genotype: treatment interaction. C) Prediction of mean values of parameters with significant genotype: treatment interaction. Abbreviations: Number of lateral roots in the first segment (NoLat 1st) or second segment (NoLat 2nd), length of representative lateral root in the first segment (MedLat 1st) or second segment (MedLat 2nd), maximal lateral root length in the first segment (MaxLat 1st) or in the second segment (MaxLat 2nd), elongation rate crown roots (ERCr), length of crown roots at solution change (intercept; ICCr), embryonic root dry weight (DWER), crown root dry weight (DWCR), chlorophyll measurements (SPAD, measured leaf area (LAm), shoot dry weight (DWS), number of pixels specifying the leaf area (LAPix), shoot pixel count at solution change (intercept; ICS), shoot pixel count development (ERS), N content in the leaf in % of total dry weight (N in %)