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Recent advances in Transformer technology for agriculture: A comprehensive survey

文献类型： 外文期刊

作者： Xie, Weijun 1 ; Zhao, Maocheng 1 ; Liu, Ying 1 ; Yang, Deyong 3 ; Huang, Kai 4 ; Fan, Chenlong 1 ; Wang, Zhandong 1 ;

作者机构： 1.Nanjing Forestry Univ, Coll Mech & Elect Engn, Nanjing 210037, Peoples R China

2.Nanjing Forestry Univ, Natl Engn Res Ctr Biomat Mech & Elect Packaging Pr, Nanjing 210037, Peoples R China

3.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

4.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing 210014, Peoples R China

关键词： Transformer; Agriculture; Deep learning; Advances; Challenges

期刊名称：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE （ 影响因子：8.0； 五年影响因子：7.7 ）

ISSN： 0952-1976

年卷期： 2024 年 138 卷

页码：

收录情况： SCI

摘要： Intelligent agriculture is critical for guiding agricultural production and enhancing efficiency through early disease diagnosis, yield estimation, automatic harvest, and postharvest efficient treatment. The conventional methods, including manual, image processing, and CNN (convolutional neural network), have some shortcomings of high labor consumption, subjectivity, poor robustness, and low efficiency. Transformer, one of the latest technological advances in deep learning, has gained widespread adoption in agriculture since its universal modeling capabilities. This paper is the first comprehensive survey of the recent advancements in Transformer- based models within the agricultural domain. Six research questions are proposed and addressed by reviewing relevant literature from different aspects. Two types of Transformer-based models (pure and hybrid Transformers) are reviewed to outline the architecture of Transformer-based models adopted in agriculture. And different applications of Transformer-based models in agriculture are summarized to display the current development of Transformer in agriculture. It also highlights the main challenges faced by Transformer technology in agriculture and discusses the future directions for its application in agricultural sector. This survey is expected to leave readers with deeper thoughts about Transformer-based models in agriculture and help them perform in-deep explorations on Transformer-based models for agricultural applications.

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