## 1 Introduction  
Cotton is one of the dominant commercial crops in major cotton-producing countries such as China, United States, India, Pakistan, Uzbekistan and West Africa [1,2], which is not only a raw material for the textile industry, but also plays an important role in the chemical industry. The growth and production of cotton are significant to economic and social development. However, cotton disease infection is one of the main factors affecting the profitability and sustainability of agricultural management [3], so it is of great importance to monitor the cotton health conditions in a timely manner and precisely detect the disease infection severity. Among all of the cotton diseases and insect pests, cotton aphids have attracted more and more attention. The fast-growing cotton aphid is one of the most destructive sucking pests in the world, and its population increases rapidly [4,5]. The infestation of cotton aphids causes damage through direct feeding, virus transmission, and honeydew contamination. Once aphid infection occurs, the cotton leaves curl downward and have a crinkled appearance. Heavy damage to cotton may decrease photosynthesis, resulting in the stunting of seedlings and yield losses. Insecticide application is an effective method of cotton aphid control; however, the abuse of insecticides could result in the failure of disease control and even environmental pollution such as soil contamination. Therefore, it is practically urgent to monitor and control aphid infestation more effectively and efficiently so that prevention and control measures can be formulated to reduce economic losses. The traditional ground-based survey method of plant infestation usually suffers from tremendous labor costs, low efficiency, and subjective human error, which makes it difficult to accurately estimate the infection areas and severity at a large scale. The most common strategy of this ground-based survey for disease detection is to conduct visual surveys by experienced producers, who can identify subtle changes in plant phenotype such as plant color, curl of the plant leaves, etc., and scout the infected area of the crop. In comparison, the spectral information of plant growth can be collected with remote sensing technology, which not only covers a large area, but also at a relatively low cost [6; 7; 8], since the stresses induced by pests and diseases affect photosynthesis and the physical structure of plants, which result in the alteration of some characteristic bands among the different severity levels of pests and diseases [9; 10; 11; 12]. Hence, the crop damage severity caused by diseases and insect pests can be identified with the sensitive wavelength. On this basis, the spectral index (SI) combined with the sensitive spectrum is obviously related to the physiological and biochemical processes of crop infection. A typical severity identification of crop infestation based on hyperspectral remote sensing technology mainly includes the steps of sensitivity feature selection and classification model determination. In the step of sensitivity feature selection, the features can be reflectance or transformed reflectance data (derivative, continuum removed, etc.) and the vegetation index [13], in which index construction is a simple and effective method and numerous indices have been formulated to detect the infection of plants by using remote sensing technologies [14]. Then, the extracted features are usually input into the classical machine learning-based classifier to determine the crop health status. The commonly used classification methods in the model determination step include random forest (RF) [15], support vector machine (SVM) [16], K-nearest neighbor (KNN) [17], etc. These classification models are usually trained on a labeled dataset and the labels of the test dataset are determined by supervised learning. For example, the iterative self-organizing data analysis techniques algorithm (ISODATA) was applied to recognize root rot infested areas of cotton [18]. [PERSON] et al.