# Research on Identification Technology of Field Pests with Protective Color Characteristics
## 1 Introduction  
Accurate identification of field pests can provide basic data for scientific pest control. It is an essential prerequisite for effective pest investigation, pest prediction, and accurate pest killing [1; 2; 3], as well as a critical foundation for appropriate pesticide application, contributing to decision-making significance for integrated pest control [4; 5; 6]. In recent years, the automatic pest identification method based on digital image processing technology has become a research hotspot for experts and scholars [7; 8; 9]. The traditional machine learning technology mainly includes three steps: image preprocessing, feature extraction, and pest identification [10; 11]. [PERSON] et al. [12] proposed a method to identify thrips using the SVM (Support Vector Machines) method with region index and intensify as the color index. The average error of the classification was less than 2.25%. [PERSON] et al. [13] developed a rice light-trap insect imaging system to automate rice pest identification. The experimental results revealed that the average accuracy of the identification of the four species of Lepidoptera rice pests was 97.5%. [PERSON] et al. [14] designed an invariant local feature-based insect classification method to automatically classify certain common insects in orchards. Although the traditional machine learning technology has made great progress in pest identification, its identification effect depends on the effect of feature extraction and the performance of the selected classifier, resulting in weak generalization ability and poor robustness of its identification model [15]. Agriculture field pests are a kind of visual target with small sizes and diverse posture changes. Additionally, its identification environmentis complex. Since most field pests have the characteristics of protective color (such as _Pieris rapae_), the identification model of deep learning with strong generalization ability is more suitable for field pest identification. This method adopts a convolutional layer, activation layer, normalization layer, and pooling layer to continuously superimpose, automatically extracts the characteristics of pest, and recognizes pests through the fully connected layer [16; 17]. [PERSON] et al. [18] proposed a classification algorithm based on feature optimization to identify rice planthoppers and reached the identification accuracy of 96.19%. [PERSON] et al. [19] improved the Faster R-CNN (Convolutional Neural Networks) model by replacing the VGG16 (Visual Geometry Group) with the depth residual network (ResNet50) to identify aphids and leaf miners on sticky cards. The results suggested that the precision of the improved Faster R-CNN model reached 90.7%. [PERSON] and [PERSON]. [20] compared three widely used deep learning meta-architectures (Faster R-CNN, SSD (Single Shot MultiBox Detector) Inception, and SSD Mobilenet) as object detection for selected flying insects, and Faster R-CNN meta-architecture presented the most outstanding performance with an accuracy of 95.33%. [PERSON] and [PERSON] [21] proposed an efficient deep CNN model to classify insect species on three publicly available insect datasets. [PERSON] et al. [22] designed a multi-class insect identification method for yellow sticky paper and obtained it from wireless cameras using cascaded convolutional neural networks. The multi-class insect classifier had an accuracy of 86-92%. Although the identification accuracy of the above methods is high, most of them are for the identification of pests on the sticky card, or for the identification of pests with large differences between target and background. At present, there are few studies on field pest identification with protective color characteristics. _Pieris rapae_ and its host plant (cabbage) with a similar color to _Pieris rapae_ were selected as experimental objects in this paper (Figure 1).