Machine learning-based hyperspectral wavelength selection and classification of spider mite-infested cucumber leaves.

Abstract

Two-spotted spider mite (Tetranychus urticae) is an important greenhouse pest. In cucumbers, heavy infestations lead to the complete loss of leaf assimilation surface, resulting in plant death. Symptoms caused by spider mite feeding alter the light reflection of leaves and could therefore be optically detected. Machine learning methods have already been employed to analyze spectral information in order to differentiate between healthy and spider mite-infested leaves of crops such as tomatoes or cotton. In this study, machine learning methods were applied to cucumbers. Hyperspectral data of leaves were recorded under controlled conditions. Effective wavelengths were identified using three feature selection methods. Subsequently, three supervised machine learning algorithms were used to classify healthy and spider mite-infested leaves. All combinations of feature selection and classification methods yielded accuracy of over 80%, even when using ten or five wavelengths. These results suggest that machine learning methods are a powerful tool for image-based detection of spider mites in cucumbers. In addition, due to the limited number of wavelengths, there is also substantial potential for practical application.

Plant phenotyping relevance

キュウリ葉のハイパースペクトル計測、波長選択、機械学習分類を用いてダニ被害状態を直接推定する方法が研究の中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The paper's hyperspectral cucumber leaf data and Python/Colab analysis code are not publicly deposited; the authors state all data is contained within the manuscript, and no repository, dataset, or code URL is provided. The only URLs mentioned (Google Colab intro, cited references) are generic or prior work, not paper-

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