Institute of Agricultural Engineering, Tropics and Subtropics Group, University of Hohenheim, Stuttgart, Germany.
Wolution GmbH & Co. KG, Munich, Germany.
Exp Appl Acarol. 2024 Oct;93(3):627-644. doi: 10.1007/s10493-024-00953-0. Epub 2024 Aug 23.
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.
二斑叶螨（Tetranychus urticae）是一种重要的温室害虫。在黄瓜中，大量的虫害会导致叶片完全失去同化表面，从而导致植物死亡。由叶螨取食引起的症状会改变叶片的光反射，因此可以通过光学检测进行探测。机器学习方法已经被用于分析光谱信息，以区分番茄或棉花等作物的健康叶片和受叶螨侵害的叶片。在这项研究中，机器学习方法被应用于黄瓜。在受控条件下记录叶片的高光谱数据。使用三种特征选择方法来识别有效波长。随后，使用三种有监督的机器学习算法对健康叶片和受叶螨侵害的叶片进行分类。即使使用十个或五个波长，所有特征选择和分类方法的组合都达到了 80%以上的准确率。这些结果表明，机器学习方法是一种基于图像的黄瓜叶螨检测的有力工具。此外，由于波长数量有限，在实际应用中也有很大的潜力。
