Strawberry foliar anthracnose assessment by hyperspectral imaging

Abstract

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 accuracy with both SDA and CM. Strawberry farmers could profit greatly from this technology.

Plant phenotyping relevance

イチゴ葉の病徴・感染段階という植物状態を、ハイパースペクトル／マルチスペクトル画像と分類手法で非破壊推定する方法が研究の中心であり、精度評価と波長選択も行っている。

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