Multimodal Application of GAN in the Image Recognition of Wheat Diseases and Insect Pests

“Food is the most important thing for the people”, Food is intricately linked to both the national economy and the livelihood of the people, serving as a vital material for our daily existence. Wheat, standing as one of the three core grain crops, holds paramount importance in safeguarding national food security. However, the wheat planting process remains constantly exposed to a diverse array of environmental factors, ranging from the intensity of light to fluctuations in temperature, soil fertility, fertilizer application methods, and water availability. Occasionally, these variables trigger diseases and insect infestations that can seriously affect wheat yield and quality if not promptly and effectively addressed. Therefore, it is imperative to manage these challenges in a timely and effective manner, ensuring the safety and integrity of wheat production, which in turn guarantees the stability of our national food supply. Traditional methods of manual detection of pests and diseases mainly rely on naked eye observation and manual statistics. Such solutions are highly subjective, have low timeliness, and difficult to unify precision. With the development of computer technology and deep learning, more and more research and applications have been carried out to address the shortcomings of traditional manual detection methods. In this study, deep learning is combined with the application of disease and insect pest recognition. Studying wheat powdery mildew, scab, leaf rust, and midge, convolutional and capsule networks are investigated for pest recognition, establishing an image recognition system for wheat diseases and pests.

@article{Li2024,
  title     = {Multimodal Application of GAN in the Image Recognition of Wheat Diseases and Insect Pests},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Bing Li and Shaoqing Yang and Zeqiang Wang},
  doi       = {10.14569/IJACSA.2024.01506105},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506105}
}