| 刊名 | Plant Diseases and Pests |
| 作者 | Shengjiu JIANG， Qian WANG |
| 作者单位 | Shaoyang Industry Polytechnic College |
| DOI | 10.19579/j.cnki.plant-d.p.2026.01-02.007 |
| 年份 | 2026 |
| 刊期 | 1 |
| 页码 | 30-34 |
| 关键词 | Agricultural disease image, Classification algorithm, Deep learning, Research Review |
| 摘要 | In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation. |
