Nemaplot hyperspectral data analysis and population modellingEvaluation reinvented


Further explanation for the use and interpretation of canonical distances


The canonical distance provides a measure of a quantitative comparison of different experimental designs, condense the test conditions and define the intensity of treatment factors on a dimensionless scale. As larger this value as larger the differences and therefore the treatment effect.

The canonical distances are the main focus of our techniques concerning the quantitative analysis of hyperspectral signatures. The canonical distances are the result of a cascade of statistical procedures in terms of model fitting and multivariate statistics. They describe the average vectors within the discriminant area or space obtained from the discriminant scores of the spectra.

Why is this value most interesting?

This dimensionless value quantifies the difference between two or more traits or classes of traits. It demonstrates the relative difference between two traits as an universal number over all treatments. The spectra contain information about both the experimental design as well as the standard number for the experimental run. Similar to a multiple mean comparison test the distance addresses the absolute size of the treatment effect in terms of the length of the arrow. Distances larger than 2 generally give evidence for a significant difference (varies with the variance). Additionally the variance is observable and open for related interpretations.

Canonical distance
Fig 1: Canonical distances of vine leaf pathogens
Example of pathogens on vine leaves, fig. 1, measurements were taken before any symptoms have been visible: The distance between downy mildew (P. viticola) infected plants and the control is above 3, which is a significant value. In contrast the figure shows the position of another pathogen, black rot (G. bidiwellii), again before symptoms have been noticed. Also the distance is significant, but smaller than compared to P. viticola. The inoculated plants are suffering from a stress (pathogen) and the distances determine the degree of stress.
Multifactor experiment analysis
Fig. 2: Analysis of multifactorial experiments

Interpretation of a multifactor experiment (fig. 2): The first factor (blue area) is most distinguishable. The dominance is interfering with the two other factors. Inside the 2nd factor (green area) as well the 3rd factor (orange area) are no differences, the related distances are too small. Discussing a tendency, factor 2 and 3 are slightly different, the distances approaching the boundaries of decision making, but the distances are below the significance limit.
Impact of nematodes and variety effekt
Fig 3: Impact of nematodes and variety effect
Interpretation of a two way experiment: The 1st factor (variety) shows most obvious a discrimination, the boundaries of the variety are clear, the 2nd factor (nematode population density) is not different. The distances within a group are just marginal. An allocation to population density classes (low, medium, high) is not possible.
Impact of nematodes and variety effekt
Fig 4: Impact of nematodes and variety effect with predetermined density classes
Same experiment as before, different year, presented in 12 combined classes with interaction. The existing nematode population are partitioned in four classes (low, medium, high, very high) and linked to the varieties (1-4 susceptible variety; 5-8, resistant variety; 9-12, tolerant variety). The hyperspectral measurements from the sugar beet canopy allow conclusion about the nematode density dependent on the variety characteristics. The susceptible variety is stronger affected by the nematode, while the nematode effect is not that clear for the tolerant / resistant varieties.
hyperspectral trait recognition
Fig 5: Trait regcognition on canonical distance spaces and vector rotation
Rotating the vectors within the area helps to discriminate the second factor visually out of large sets of data. The vector of the control is set to a zero base line, all other distances are corrected and compared with respect to length and position within the discriminant area. The example is used to differentiate traits of new varieties from hyperspectral signatures. We can construct a confidence circle within the discriminant area. The vectors of new varieties inside the circle are not different from the traits of the control variety. Lines far out of the circle are significant different from the control, but do not satisfy the breeding objectives, the genetic distance is too large. Most interesting for the further breeding program are lines with vectors just touching the confidence circle and lengths below the control.
Time series of hyperspectral measurements
Fig. 6:Time series of hyperspectral measurements, ex. salt stress progress
The advantage of non-invasive sensors include the ability for "endless" repeated measurements of the same object. The dynamics of experiments are clearly shown by the canonical distances (y-axis) put on the time axis. The example demonstrates the effect of salt stress. The vary distances over time are fitted to a logistic function and present the dynamics of an increasing stress.
Mildew dynamics
Fig. 7: Time series of hyperspectral measurements, ex. mildew disease progress
Disease progress in terms of canonical distances. Canonical score values cannot always be transfered 1:1 to known situations. Disease progress follows mostly exponential processes. On the scales of canonical distances a logistic model is appropriate. The systems detect pathogen infection earlier, but approaches an upper saturation or asymptote earlier than the real disease.



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