Abstract
Researchers have shown increasing interest in hyperspectral imaging for detecting potato late blight disease (Phytophthora infestans). Because it is difficult to get accurate spectral signatures of disease development in field conditions, especially at early disease stages, previous works focused on laboratory measurements under controlled conditions. However, the extrapolation of results from a laboratory to a field setting has proven difficult. The current work evaluates the use of laboratory hyperspectral data to train an in-field detection model for potato late blight. A hyperspectral training library was constructed from six detached leaf trays, containing 8585 spectra labelled into a healthy class and five progressive stages of disease development. After smoothing and normalisation, a logistic regression model was trained on 70.0% of this data, with 30.0% reserved for validation. Twelve hyperspectral images taken in field conditions were then classified, for two potato cultivars (susceptible and resistant to late blight), at high and low disease pressure. The classification accuracy of laboratory data was 94.1%, which was not sufficient to detect field symptoms, using infield collected dataset. When spectra pre-processing was changed by including first derivation and adopting a new normalisation strategy, a new model resulted in a lower classification accuracy of 80.8%, validated on labelled laboratory spectra, but was able to detect symptoms in field conditions. The correlation between visual disease scoring and the classification result of the field disease model yielded an R2 value of 0.985. It could be concluded that it was possible to train a model on laboratory data for in-field disease detection.
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Data availability
The datasets and code generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- FDR:
-
False discovery rate
- FNR:
-
False negative rate
- FPR:
-
False positive rate
- NDVI:
-
Normalized difference vegetation index
- NIR:
-
Near-infrared
- NPV:
-
Negative predictive value
- PCA:
-
Interprovinciaal proefcentrum voor de aardappelteelt
- PPV:
-
Positive prediction value
- TNR:
-
True negative rate
- TPR:
-
True positive rate
- VIS:
-
Visible
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Acknowledgements
A big thank you to the technicians at PCA (Jenny Heuvick, Bart De Vriese and Pascal Dupont) for their help during field work and disease scoring of the trial fields at PCA. We are also grateful to the people at the Bottelare experimental farm, especially Kevin Dewitte, for helping with the collection of laboratory data.
Funding
Authors acknowledge the financial support received from the Research Foundation—Flanders (FWO) for Odysseus I SiTeMan Project (Nr. G0F9216N).
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Appeltans, S., Pieters, J.G. & Mouazen, A.M. Potential of laboratory hyperspectral data for in-field detection of Phytophthora infestans on potato. Precision Agric 23, 876–893 (2022). https://doi.org/10.1007/s11119-021-09865-0
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DOI: https://doi.org/10.1007/s11119-021-09865-0