## 4 Discussion  
With the virus transmission of aphids in a cotton field, ruinous damage of yield and cotton quality is inevitable, which causes huge economic loss and environmental pollution problems [3]. The automatic detection method of crop disease severity level has become increasingly important in the field of agricultural diseases and pests [2; 7; 10]. The crops have been treated with inoculum in most of the studies on crop diseases and pests [23; 33], which has made it easier to detect the infected samples. In this study, the cotton field was in a natural environment, without any inoculum treatment, and the cotton aphids were the result of natural processes. Previous studies have explored the discrimination of cotton aphids at the leaf scale and showed a clear correlation between the leaf reflectance and cotton aphid DI [34]. This correlation provides a basis for the exploration of the automatic grading method of aphid damage on a larger scale. In this study, the natural environment and monitoring scale made the automatic grading method more challenging and valuable. Traditionally, the existing binary classifiers or threshold segmentation methods have been adopted to distinguish between healthy and infected samples, but not the multiple disease severity levels. Meanwhile, grading disease severity was regarded as a multi-class classification problem in machine learning, and it was mainly considered that there was no correlation among multiple levels. However, the severity levels of the crop diseases and insect pests were not independent classes without any inter-class correlation, and the disease severity classification based on existing classifiers in machine learning usually had weak interpretability. The automatic disease severity grading approach proposed in this study could help to fill this gap. The proposed approach mined the inherited relations among different severity levels with RISIs and calculated the level intervals, which gave it stronger interpretability than the traditional multi-class classifiers, and the threshold setting between the adjacent disease levels made it easier to determine the severity grades of the diseases and pests. In grading the disease severity caused by cotton aphids, the damage severity could be divided into five ranges based on sensitive RIRSI (R732, R878, R712), and 0.006, 0.018, 0.029, 0.041 were adjacent thresholds for interval division. As far as multi-class classifiers in grading cotton aphids, pairwise class discrimination was needed to optimize the classifier parameters, that is, 10 inter-class discriminations, and it was difficult to obtain the level range of high dimensional data, but the proposed method only distinguished between the adjacent levels and thresholds and level range could be acquired with five discriminations. A large number of contiguous narrow bands in disease detection resulted in data redundancy, which increased the difficulty in data processing [19]. It was significant to develop a selection method of sensitive spectrums or spectral indices and use a limited number of wavebands that were strongly correlated to crop diseases in grading the damage severity level. In this study, it was difficult to detect the cotton aphid severity level due to the large number of hyperspectral wavebands with the full range of 345-1075 nm. The complex problem of sensitive waveband selection was converted to the distance ranking based on reconstructed spectral indices with healthy samples, which was a huge difference between this study and other studies with regard to the severity classification of disease and infection [14]. The proposed dimension reduction method by selecting the sensitive waveband was evaluated using three types of hyperspectral indices on all possible two- and three-band combinations. As shown in Table 3, among the sensitive band combination selected from each type of spectral indices, the minimum OA, AA, and Kc values were 77.8%, 72%, and 0.714, respectively, and the best performance with OA = 94.4%, AA = 90%,