From Convolutional Networks to Vision Transformers: Evolution of Deep Learning in Agricultural Pest and Disease Identification

Abstract

Traditional pest and disease identification methods mainly rely on manual detection or traditional machine learning techniques, but they have obvious deficiencies in terms of their accuracy and generalisation ability. In recent years, deep learning has gradually become the preferred solution for the intelligent identification of pests and diseases by virtue of its powerful automatic feature extraction and complex data-processing capabilities. In this paper, we systematically present the application of traditional machine learning methods in pest and disease identification and their limitations, and focus on the research progress of deep learning methods, covering three mainstream architectures: convolutional neural network (CNN), Vision Transformer model and CNN–Transformer hybrid model. In addition, this paper provides an in-depth analysis of the key challenges currently faced in the field of pest recognition, including the problems of small-sample learning, complex background interference and model lightweighting, and further propose solutions for future research to provide theoretical references and technical guidance for the development of related fields.