Advanced agricultural disease image recognition technologies: A review

Agricultural disease image recognition has an important role to play in the field of intelligent agriculture. Some advanced machine learning methods associated with the development of artificial intelligence technology in recent years, such as deep learning and transfer learning, have begun to be used for the recognition of agricultural diseases. However, the adoption of these methods continues to face a number of important challenges. This paper looks specifically at deep learning and transfer learning and discusses the recent progress in the use of these advanced technologies for agricultural disease image recognition. Analysis and comparison of these two methods reveals that current agricultural disease data resources make transfer learning the better option. The paper then examines the core issues that require further study for research in this domain to continue to progress, such as the construction of image datasets, the selection of big data auxiliary domains and the optimization of the transfer learning method. Creating image datasets obtained under actual cultivation conditions is found to be especially important for the development of practically viable agricultural disease image recognition systems.