Abstract
The benefits and limitations of crop stress detection by hyperspectral data analysis have been examined in detail. It could thereby be demonstrated that even a differentiation between healthy and fungal infected wheat stands is possible and profits by analyzing entire spectra or specifically selected spectral bands/ranges. For reasons of practicability in agriculture, spatial information about the health status of crop plants beyond a binary classification would be a major benefit. Thus, the potential of hyperspectral data for the derivation of several disease severity classes or moreover the derivation of continual disease severity has to be further examined. In the present study, a state-of-the-art regression approach using support vector machines (SVM) has been applied to hyperspectral AISA-Dual data to derive the disease severity caused by leaf rust (Puccinina recondita) in wheat. Ground truth disease ratings were realized within an experimental field. A mean correlation coefficient of r=0.69 between severities and support vector regression predicted severities could be achieved using indepent training and test data. The results show that the SVR is generally suitable for the derivation of continual disease severity values, but the crucial point is the uncertainty in the reference severity data, which is used to train the regression.
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Claims 3
Claim · model-written summary
Hyperspectral data differentiated healthy from fungal infected wheat stands.
- Taxon
- wheat
Triticum aestivume1 - Outcome
- differentiation between healthy and fungal infected wheat standse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“It could thereby be demonstrated that even a differentiation between healthy and fungal infected wheat stands is possible”
Claim · model-written summary
Support vector regression predicted disease severities correlated with ground truth severities at a mean r=0.69 in an experimental field.
- Taxon
- wheatTriticum aestivume2
- Interactor
- Puccinina reconditae2pathogen
- Outcome
- mean correlation coefficient of r=0.69 between severities and support vector regression predicted severitiese3
- Value
- r=0.69e3
- Qualifier
- using indepent training and test datae3
- Setting
- experimental fielde4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e2“to derive the disease severity caused by leaf rust (Puccinina recondita) in wheat”
e3“A mean correlation coefficient of r=0.69 between severities and support vector regression predicted severities could be achieved using indepent training and test data”
e4“Ground truth disease ratings were realized within an experimental field”
Claim · model-written summary
Support vector regression is generally suitable for deriving continuous wheat disease severity values, but uncertainty in the reference severity data is the crucial point.
- Taxon
- wheatTriticum aestivume2
- Outcome
- suitable for the derivation of continual disease severity valuese5
- Qualifier
- the crucial point is the uncertainty in the reference severity datae5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e2“to derive the disease severity caused by leaf rust (Puccinina recondita) in wheat”
e5“The results show that the SVR is generally suitable for the derivation of continual disease severity values, but the crucial point is the uncertainty in the reference severity data, which is used to train the regression”