# Citrus Pest Identification Model Based on Improved ShuffleNet
## 1 Introduction  
Citrus, the most extensively cultivated fruit tree variety in China, is predominantly distributed in the southern regions of the country owing to its high profitability. In 2021, the annual citrus production in China reached 57.3216 million tons, representing roughly one third of the global total output [1; 2]. Due to the excessive application of pesticides, expansion of citrus cultivation areas, and global climate change, the incidence and spread of citrus pests and diseases are worsening. China may encounter new challenges in controlling citrus pests. Currently, traditional pest identification methods include manual identification [3] and instrument-based identification. The former method exhibits strong subjectivity, low efficiency, and high manual cost consumption [4; 5; 6], whereas the instrument recognition method is susceptible to external environmental factors, hardware devices, and software systems. These shortcomings may result in the delayed detection and treatment of pests, causing crop damage and reduced yields. Deep learning image recognition technology can effectively address these issues, facilitate the diagnosis and prevention of crop pests, and promote rapid advancements in agriculture [7; 8; 9]. Common deep learning networks can be classified into two categories, including large networks (e.g., ResNet [10], VggNet [11]) and lightweight networks (e.g., SqueezeNet [12], ShuffleNet [13], and MobileNet [14]). In research on large-scale networks, [PERSON] et al. [15] introduced a G-ECA Layer structure into the DenseNet201 model to enhance its feature extraction capability. The new model achieved an accuracy of 83.52% in recognizing five categories of images of rice diseases and insect pests. In addition, [PERSON] et al. [16] integrated thearchitecture of SeparableConv with deep separable convolutional layers based on the Xception network, achieving complete decoupling of spatial and cross-channel correlations. This model improved the effectiveness without increasing the network complexity. Furthermore, it achieved an identification accuracy of 81.9% in recognizing four citrus diseases and pests, such as woodlice and fruit flies. [PERSON] et al. [17] proposed an ensemble-based model using transfer learning, where an amalgamation of pretrained models was experimented with. The ensemble model comprising VGG16, VGG19, and ResNet50, followed by a voting classifier ensemble, yielded the most promising results, achieving an accuracy of approximately 82.5%. [PERSON] et al. [18] used an R-CNN model based on a 33-layer ResNet main trunk network to identify citrus huanglongbing, red spider infection, and darker disease. Although there were few convolutional layer networks selected, they were still able to achieve a high recognition accuracy. The studies under discussion encountered two main challenges: the complexity of the models and their suboptimal recognition accuracy. Recognized for their minimal computational demands and robust learning capacities, lightweight networks also offer modest memory usage and adaptability. These advantages have made them increasingly preferred for pest identification tasks. In their study, [PERSON] et al. [19] enhanced EfficientNet. They integrated a coordinated attention mechanism and implemented a hybrid training approach. This approach combines data augmentation with the Adam optimization algorithm. It effectively improves the model's generalization capabilities. Despite these improvements, the model attained a modest accuracy of 69.45% with 5.38 M parameters. [PERSON] et al. [20] also sought to improve model performance. They augmented MobileNet V2 by incorporating the efficient channel attention (ECA) mechanism. This enhancement improved the network's capability for cross-channel interaction and feature extraction. Their model achieved a classification accuracy of 93.63% for citrus pests, utilizing 3.5 M parameters. [PERSON] et al. [21] proposed an efficient training framework tailored for optimizing small-sized MobileNetV2 models.