Then, AlexNet [22] and GoogleNet models are employed to identify various classifies with an accuracy rate of 99.35%. [PERSON] [23] also collected 87,848 leaf pest pictures of different fruits and vegetables such as apples and potatoes and adjusted the fully connected pooling and multiple learning rate training of VGGNet, which effectively reduced the number of model parameters and improved the recognition rate up to 99.53%. Similarly, [PERSON] et al. [24] collected over 75,200 images covering 102 types of crop insect pests and framed more than 19,000 photos to solve the problem of target detection. On this basis, feature extractors such as VGGNet and ResNet are combined with the detector modules such as SSD [25] and YOLOv3 [26] to effectively verifies the significant benefits of deep-learning technologies in insect pest classification as well as marking the area where the pests located and counting the number of them. Moreover, some studies have improved the pest recognition model in parameter lightweight and structure compression operations to accommodate the real-time application requirements of automatic robots and IoT devices. [PERSON] et al. [27] performed migrating two lightweight convolutional networks, MobileNet [28] and Inception V3 [29], to realize the pest identification of leaves. After comprehensively balancing factors such as recognition accuracy, computing speed, and network size, the optimal model is selected and transplanted to the Android system on mobile phones. Similarly, [PERSON] et al. [30] performed super-pixelization preprocessing and fully connected layer optimization on the ResNet50 network to improve the pest recognition performance of winter wheat in actual scenes. The recognition time of a single picture is within 2.8 s with an 87% recognition rate, which initially meets the application requirements. Although the above studies provide a reference and feasibility for the use of supervised deep-learning neural networks to identify plant insect pests, the efficiency and accuracy for plant pest recognition need to be improved since existing deep-learning algorithms remain challenging in a natural environment. The main problem is that the identification process of pest species in complex practical scenes is a fine-grained visual classification (FGVC) problem. As one of brand-new hot research in computer science and intelligence technology, FGVC is mainly to identify image samples belonging to multiple sub-level classes via retrieving objects under a meta-level category, which is more difficult than simple coarse-grained identification of traditional image recognition [31]. With the remarkable breakthroughs of deep-learning techniques, FGVC has enjoyed a wide range of applications in both industry and research societies for several decades, such as birds, dogs, cars, or aircraft types. Nevertheless, it is still a challengeable assignment to realize fine-grained pest identification by using deep-learning models at high precision. In detail, there are many fine-grained difficulties in identifying insect pests in complex agricultural and forestry scenarios. Firstly, in an actual planting environment, multi-source cameras are applied to collect a large number of pest images, which usually leads to the intraspecific difference phenomenon. In this regard, the same meta-level category contains vast image samples with viewpoints, illumination, definition, positions in significantly different. This interference influence of data sources and environmental factors causes that the models are easy to misjudge different samples coming from the same meta-category into other categories. Secondly, there are growth period states of different insect pests, which leads to obvious differences in the characteristics of different stages of the same kind, and different pests show certain similarities at some times.