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Advanced agricultural disease image recognition technologies: A review

文献类型： 外文期刊

作者： Yuan, Yuan 1 ; Chen, Lei 1 ; Wu, Huarui 2 ; Li, Lin 3 ;

作者机构： 1.Chinese Acad Sci, Inst Intelligent Machines, HFIPS, Hefei 230031, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Jiangsu Univ, Zhenjiang 212013, Peoples R China

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

期刊名称：INFORMATION PROCESSING IN AGRICULTURE （ 影响因子：7.4； ）

ISSN： 2214-3173

年卷期： 2022 年 9 卷 1 期

页码：

收录情况： SCI

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. (c) 2021 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).

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