Advanced agricultural disease image recognition technologies: A review

Information Processing in Agriculture

Huarui Wu¹  Lin Li²  Lei Chen³  Yuan Yuan³ 

[1] Corresponding authors.;Beijing Research Center for Information Technology in Agriculture, Beijing 100097, PR China;Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China;

关键词: Agricultural diseases;    Image recognition;    Artificial intelligence;    Transfer learning;    Deep learning;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown