# Research on Identification Technology of Field Pests with Protective Color Characteristics
## 4 Conclusions  
In this paper, a field pest identification method based on YOLOv5 and hyperspectral technology was proposed. The results have demonstrated that this method can effectively identify pests with protective color characteristics in the complex field environment.  
In the process of collecting pest images, to realize the identification of pests with protective color characteristics, obtain the _Pieris rapae_ and cabbage spectral information by hyperspectral technology before image acquisition, specific spectral characteristics of _Pieris rapae_ and cabbage can be formed on the spectral curves. Comparing these two curves to get the wavelength with the largest reflectivity difference, and an appropriate infrared filter and ring light source are selected to build an image acquisition system. In order to improve the accuracy of pest identification, we collect pest images in different situations, expanded the original pest data set by data enhancement and select the appropriate target identification algorithm (YOLOv5). The detection results of the test set showed that compared with the existing research articles [37; 38; 52; 53], the combination of YOLOv5 and hyperspectral technology can effectively identify field pests with protective color characteristics. This paper takes _Pieris rapae_ and its host plant (cabbage) as the experimental object, its mAP was 99.7%, and the average time required to detect a pest image is 0.56 s.  
Considering the future application scenario of pest identification, the current algorithm has some limitations in detection speed. In order to improve the detection speed of target detection algorithm, efficient models can be designed to accelerate the algorithms, such as decreasing the redundancy in weights by network pruning and knowledge distillation. While improving the detection speed, how to ensure the detection accuracy is also an aspect to be considered in the future. In addition, although only one pest with protective color characteristics (_Pieris rapae_) is considered in this paper, the relevant literature has proved that the near-infrared technology can distinguish the target objects with similar appearance characteristics and background [24], so this method can still be used to identify other pests with protective color characteristics and their host plants. For different pests, only the wavelength with the largest spectral reflectance difference between pests with protective color characteristics and their host plants needs to be obtained through hyperspectral test, so as to select the appropriate infrared filter. Replace the original infrared filter on the original image acquisition platform. That is, almost the same setting can be implemented in many different situations. In the future, other pests with protective color characteristics will be tested to further improve the universality of this method.  
Figure 12: P-R curve.  
**Author Contributions:** Conceptualization, Z.H. and Y.X.; methodology, Z.H., Y.X. and Y.L.; software, Z.H. and Y.L.; validation, Z.H., Y.L. and A.L.; investigation, Z.H., Z.L., X.D. and X.L.; resources, Y.X.; data curation, Z.H., X.L. and Z.T.; writing--original draft preparation, Z.H., Y.L. and Y.X.; writing--review and editing, Y.X.; visualization, Z.H.; supervision, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province of China and grant number 2021J30363\", and the Scientific Research Fund of the Hunan Provincial Education Department of China and grant number 19A224.  
Not applicable.  
**Data Availability Statement:** All data are presented in this article in the form of figures and tables.  
We gratefully acknowledge the Yunyuan Scientific Research Base (Hunan Agricultural University, Changsha) for providing us with insects and cabbage leaves.  
The authors declare no conflict of interest.