Detection of spider mite infestation via hyperspectral imaging and deep neural networks with spatial-spectral attention.

Early detection of spider mite emergence is highly challenging due to the lack of visible symptoms and subtle physiological changes. To address the rapid monitoring of pest mite damage, this study provided a method based on hyperspectral imaging combined with a joint spatial-spectral attention (SSA) mechanism in a deep convolutional neural network (DCNN) for automated detection of pest mite infections. Leaves infested with varying degrees of spider mites Tetranychus urticae Koch (Acari: Tetranychidae) were captured daily to obtain multi-band spectral information of hyperspectral images. After data preprocessing, the joint SSA mechanism was applied to weigh and optimise the spatial and spectral features of the images, focusing specifically on infested regions. Furthermore, the super-pixel principal component analysis method (SPCA) dimensionality reduction method was applied to reduce model complexity and enhance classification accuracy. The spatial attention module automatically adjusted the weights of critical regions in the image, ensuring the network concentrated on the infected areas, while the spectral attention module improved sensitivity to the unique spectral features of infected regions. The proposed method can significantly enhance the accuracy of identifying mite infected areas, particularly in detecting mild infections of the leaves. Compared with traditional approaches, our proposed SPCA + DCNN + SSA model achieved notable improvements in classification accuracy and robustness, yielding the highest OA (up to 99.1% for specific severity levels) and Kappa coefficient (0.989). Importantly, it drastically reduced misclassification in the highly challenging mild infection stages.