## 1 Introduction
[19] proposed an automatic crop disease severity classification method based on vegetation index normalization with six typical vegetation indices on cotton fields infected with root rot. [PERSON] et al. [20] developed a phagosphaeria leaf spot identification method based on random forest, which could be used for early disease detection in maize fields with the extracted six key features. [PERSON] et al. [21] used genetic algorithms (GA) to identify the optimal combination of six bands from 240 hyperspectral bands, and then classified the combination with SVM for the early-stage detection of charcoal rot disease in soybean stems. [PERSON] et al. [22] adopted the maximum likelihood algorithm to classify Landsat 5 Thematic Mapper (TM) images in Texas to discriminate the wheat streak mosaic virus. In particular, [PERSON] et al. [23] established an optimum dual-green vegetation index to improve the detection of wheat powdery mildew, which provided a new idea and analytical method for the spectral monitoring of wheat diseases, and also indicated that the spectral monitoring of healthy wheat in constructing a dual-green vegetation index significantly improved the disease estimation precision compared with the optimal common vegetation index. [PERSON] et al. [24] constructed a sensitive index for measuring spectral differences between leaves infected by MDMV and healthy leaves and also developed the classification model with integrated linear discriminant analysis (LDA) with SVM to detect the damage severity caused by the maize dwarf mosaic virus. Despite the promising results in previous studies, spot-level hyperspectral measurements have rarely been used to detect cotton aphids, and the crops are usually treated with diseases or insect inoculum, which can make the infection severity of the crops relatively uniform. However, in a natural environment, crop diseases and pests often originate from one place and then spread to other regions. Moreover, it is challenging to analyze and reconstruct the hyperspectral indices to provide valuable information indicating the physiological and biochemical processes of pest and disease damage on plants. In addition, the severity level identification of pest and disease damage in crops mainly relieson classification models. The research has paid more attention on selecting the effective model based on the influence of different classification models, but not much effort has been taken to analyze the inherited relations among different classes or grades, and the severity class is considered as independent class, however, different classes in disease damage correspond to specific disease severity ranges. Ignoring the inter-class associations will affect the classification performance and increase the complexity of the inter-class comparison. Therefore, to address the issue presented above, we constructed six typical spectral indices from the full spectral bands, which were then reconstructed based on the comparison with healthy samples. Furthermore, a novel approach to select sensitive spectral indices was proposed, and simultaneously, a corresponding method of setting thresholds between adjacent disease levels instead of selecting the classification model was established to identify the severity caused by cotton aphids.