Strawberry foliar anthracnose assessment by hyperspectral imaging

•Hyperspectral imaging has proven to be an effective non-destructive method for assessing strawberry foliar anthracnose.•The incubation stage, in which symptoms are not yet visible, can be distinguished.•Several hyperspectral imaging analysis methods were investigated using 3 duplicate sets of exper...

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Bibliographic Details
Published in:Computers and electronics in agriculture 2016-03, Vol.122, p.1-9
Main Authors: Yeh, Yu-Hui, Chung, Wei-Chang, Liao, Jui-Yu, Chung, Chia-Lin, Kuo, Yan-Fu, Lin, Ta-Te
Format: Article
Language:English
Subjects:
Accuracy
Agriculture
Anthracnose
Classification
Hyperspectral imaging
Imaging
Plant disease
Spectra
Strawberries
Visible near-infrared
Wavelengths
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Summary:•Hyperspectral imaging has proven to be an effective non-destructive method for assessing strawberry foliar anthracnose.•The incubation stage, in which symptoms are not yet visible, can be distinguished.•Several hyperspectral imaging analysis methods were investigated using 3 duplicate sets of experiments.•Significant wavelengths for strawberry foliar anthracnose are 551, 706, 750 and 914nm. Hyperspectral imaging provides comprehensive spectral and spatial information about observed objects. This technology has been applied to many fields, such as geology, mining, surveillance and agriculture. Strawberry qualities have been examined using hyperspectral imaging in several studies. However, none of the previous literature presented a non-destructive method for diagnosing the infection stages of anthracnose, a devastating disease for strawberries. This study examined strawberry foliar anthracnose using three different hyperspectral imaging analyzing methods: spectral angle mapper (SAM), stepwise discriminant analysis (SDA) and self-developed correlation measure (CM). Three different infection stages, including healthy, incubation and symptomatic stages, were investigated using these methods. The incubation stage is a stage at which the symptoms are still not yet visible. The three infection stage classification results were promising, with a classification accuracy of approximately 80%. For two infection stage classification (healthy and symptomatic stages), an average accuracy of high 80% was attained. In fact, an average accuracy of 93% was achieved by SDA for two-stage classification. This study not only proves the feasibility of hyperspectral imaging for diagnosing strawberry foliar anthracnose infection, but also identifies a smaller set of significant wavelengths at which similar classification performance was accomplished. For significant wavelength selection, partial least squares (PLS) regression is an standard wavelength selection method and it was applied to be compared with SDA and CM. Wavelengths of 551, 706, 750 and 914nm formed the multispectral imaging observing bands that showed an accuracy of 80% when classifying the three infection stages. Therefore, using either hyperspectral or multispectral imaging to detect anthracnose infected foliar areas is more practical and efficient than classic destructive methods. In particular, early detection (the incubation stage), something that cannot be seen via naked eyes, reaches 80% classification accur
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2016.01.012