## 1 Introduction
Based on the results they obtained, they proposed a landscape membership-based random forest algorithm to quantify landscape structure and hydrological regimen of locust habitats at the patch level. [PERSON] et al. [16] extracted surface temperature, leaf area index, and soil moisture root zones from the soil moisture active passive satellite data as environmental factors and analyzed their correlations with species' occurrences of desert locusts using species distribution models. However, developing satellite remote-sensing observation techniques alone cannot meet the requirements for rapid and accurate locust plague prevention and control. Satellite remote-sensing observations are affected by factors such as orbits and weather conditions and, therefore, cannot meet the long-duration and high-timeliness requirements of locust monitoring systems. Also, the key to using satellite remote-sensing technologies to monitor and provide warnings of locust plagues is by recognizing the relation between locust distribution density and structural changes in locust communities using the hydrothermal and vegetation growth conditions in their habitats at various regions. This requires large amounts of quadrat-scale locust instar, type, and distribution density observation data. Additionally, sample scale locust observation data are necessary in understanding the occurrence processes of locust plagues and their driving factors. Therefore, developing efficient and accurate sample scale locust information acquisition techniques plays a significant role in understanding the occurrence processes [17] and causes of locust plagues and providing rapid and accurate warnings about locust plagues. Recently, computer-vision techniques have developed rapidly and have been extensively applied in monitoring and identification of insects [18; 19]. [PERSON] et al. [20] proposed a rapid computer-vision method for monitoring adult beetles and evaluating their numbers. [PERSON] et al. [21] successfully extracted segmented images of target individuals and rapidly identified and classified a large number of specimens by equipping an insect identification system developed for the United States Department of Agriculture with an image segmentation algorithm. By improving the image identification rate by employing scale-invariant feature transforms and the LOSS algorithm, [PERSON] et al. [22] identified various pests, including _Diabrotica_ (Coleopterara: Chrysomelidae), _Aphis gossypi_ Genn., _Thrips tabaci_ L. (adult), and _Bemisia tabaci_ Genn., with high accuracy. [PERSON] et al. [23] proposed a reverse identification method for identifying pests based on leaves eaten by pests, extracted eigenvalues, and constructed a backpropagation (BP) neural network identification model. Additionally, they demonstrated that the proposed method outperforms previous methods in terms of identification efficiency and accuracy. [PERSON] [24] extracted morphological and color features of eight common farmland pests used a backpropagation (BP) neural network classifier to classify the pests and achieved an accuracy of 85.70%. [PERSON] et al. [25] identified locusts and calculated population densities by dynamically extracting color, area, and morphological features by employing an image processing technique. [PERSON] et al. [26] proposed a locust identification model based on near-infrared spectroscopy and hierarchical clustering. They examined the feasibility of this model for rapid detection of locusts in complex environments with interlaced vegetation, soil, and rocks and found that their model achieved an accuracy of up to 91.67%. [PERSON] et al. [27] formulated an improved split-merge algorithm by introducing a pulse-coupled neural network and a simplified Mumford-Shah model and applied it to the segment images of adult locusts and locust larvae. [PERSON] [28] proposed a method that involves combining a maximum-similarity region-merging algorithm with texture and color histograms to identify pests.