An Improved DCGAN-Based Recognition Enhancement Method for American Hyphantria cunea Larvae Net Curtain Image Dataset

Abstract

The fall webworm (Hyphantria cunea) poses a significant threat to agriculture, as its larvae feed on leaves and form silken webs, which can severely impact plant growth. However, the lack of specific image datasets for the larvae’s webs hinders the use of image recognition technologies in pest prevention and control. To address this issue, an enhancement method is proposed here based on an improved Deep Convolutional Generative Adversarial Network (DCGAN). This method generates a diverse set of high-quality web images, significantly expanding the existing dataset. Experimental results demonstrated that this enhanced dataset improved the robustness of recognition networks, enabling better automatic identification and precision spraying to control Hyphantria cunea. This approach not only advances automated pest monitoring in agriculture but also offers new possibilities for applying similar technologies to the identification of other plant pests.

Keywords

• american hyphantria cunea larvae net curtain
• generative adversarial network
• data enhancement
• convolutional neural network
• checkerboard effect
• plant pest control
• patent